# Volume Four — theories 18-26 (new file)
THEORIES = [
{
 "n": 18,
 "slug": "nociceptors-report-threat",
 "field": "Pain & Somatosensation",
 "kind": "Sensory physiology",
 "title": "Nociceptors Report Threat, Not Damage",
 "claim": "Nociceptor firing encodes the predicted threat to tissue rather than the current state of the tissue, so firing should track the rate of change of conditions and the reliability of the context rather than the size of a lesion.",
 "dek": "Pain nerves are named for damage. What they seem to signal is danger.",
 "evidence": [
   ("Effective connectivity work on nociception places the ascending signal within a predictive-coding framework, with the brain's model of threat shaping the response rather than the signal being a direct readout of tissue state.",
    "https://www.jneurosci.org/content/45/25/e1447242025", "Journal of Neuroscience, 2025"),
   ("Chronic pain has been modelled as a maladaptive compensation in hierarchical predictive processing, which is only coherent if the ascending signal carries a prediction rather than a measurement.",
    "https://www.sciencedirect.com/science/article/pii/S1053811924002052", "NeuroImage"),
 ],
 "tension": "Nociceptors are classified by the stimuli that activate them and are described as detectors of damage. That account leaves a large residual: firing does not track lesion size, and the same stimulus produces different firing depending on context and expectation.",
 "theory": [
   "A detector of tissue state would fire in proportion to damage. A detector of threat would fire in proportion to the probability that damage is about to occur, which depends on rate of change, on the reliability of the environment, and on prior experience.",
   "Nociceptors have the properties of the second: they adapt, they sensitise after injury in a way that outlasts the injury, and they are modulated by descending signals. Those are properties of a predictor, not a thermometer.",
   "The prediction is that firing should depend on the derivative of the stimulus more than its magnitude, and that it should be modulated by context in a way that a detector model cannot produce. It also predicts that blocking the context should reduce firing without changing the stimulus.",
   "The clinical consequence is that treating pain as a signal of damage leads to interventions aimed at the tissue, while treating it as a prediction leads to interventions aimed at the model, which is where chronic pain treatment is slowly moving."
 ],
 "prior": ("Nociceptors as damage detectors",
           "The detector account makes firing a function of tissue state. This makes it a function of predicted threat, which explains adaptation, sensitisation and context effects that a detector cannot."),
 "predictions": [
   ("P1", "Firing should depend on the rate of change of a stimulus more than on its absolute magnitude."),
   ("P2", "Context manipulations should change firing at constant stimulus."),
   ("P3", "Sensitisation should outlast the injury by a duration set by the prediction, not the tissue."),
 ],
 "refute": "Firing proportional to lesion size across contexts would restore the detector account.",
 "change": "It would reframe pain treatment around prediction, and change how nociceptor data are interpreted.",
 "sources": [
   ("Functional anatomy of nociception: connectivity in chronic pain and placebo — J Neurosci", "https://www.jneurosci.org/content/45/25/e1447242025"),
   ("Chronic pain as maladaptive compensation in hierarchical predictive processing — NeuroImage", "https://www.sciencedirect.com/science/article/pii/S1053811924002052"),
 ],
},
{
 "n": 19,
 "slug": "city-scaling-is-network-depth",
 "field": "Urban Systems & Cities",
 "kind": "Urban scaling",
 "title": "City Scaling Is Network Depth",
 "claim": "The superlinear scaling of urban output is set by the depth of the infrastructure and interaction hierarchy rather than by population size, so cities with deeper hierarchies should have larger exponents and the exponent should be predictable from network topology.",
 "dek": "Every city seems to scale the same way. The exponent may be a property of how many layers the city has.",
 "evidence": [
   ("Scaling analysis of urban infrastructure has related the exponent to the geometry and hierarchy of the network, with the infrastructure area scaling sublinearly in a way that constrains the output exponent.",
    "https://publications.pik-potsdam.de/rest/items/item_28210_4/component/file_28255/content?download=true", "PIK, urban scaling"),
   ("The hierarchy of urban road networks has been analysed from a scaling-law perspective, which supplies the topological quantity an exponent-from-depth account requires.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0264275125005165", "Cities, road network hierarchy"),
 ],
 "tension": "Urban scaling is reported as a universal law with an exponent near 1.15 for output and 0.85 for infrastructure. Universality is the puzzle: cities with very different histories and geometries produce similar exponents, and the deviations are treated as measurement error rather than as structure.",
 "theory": [
   "A city's output depends on interactions, and interactions are organised by a hierarchy: local clusters, districts, and a centre. The number of interactions available grows with the depth of that hierarchy, because each level multiplies the possible pairings.",
   "If the exponent reflects hierarchy depth, then cities with more levels should scale more steeply, and the universal value is the average depth of the sample rather than a law of cities. That makes the exponent a measurable structural parameter.",
   "The prediction is that the exponent should correlate with a measured hierarchy index, such as the number of distinct levels in the road or transit network, and that cities engineered to add a level should shift their exponent.",
   "This matters for policy because it suggests that the returns to scale are not automatic. A city that grows without deepening its hierarchy will scale like a large town, not like a metropolis."
 ],
 "prior": ("Universal urban scaling laws",
           "The law treats the exponent as a constant of urbanism. This treats it as a function of hierarchy depth, which explains the deviations and predicts that the exponent can be changed by design."),
 "predictions": [
   ("P1", "The output exponent should correlate with a measured hierarchy depth index."),
   ("P2", "Adding a hierarchy level should increase the exponent."),
   ("P3", "Cities with similar populations but different depths should differ in per-capita output."),
 ],
 "refute": "Exponents invariant to hierarchy depth across well-measured cities would restore the universal law.",
 "change": "It would make urban scaling a design variable, and change how infrastructure investment is justified.",
 "sources": [
   ("Urban scaling and infrastructure geometry — PIK", "https://publications.pik-potsdam.de/rest/items/item_28210_4/component/file_28255/content?download=true"),
   ("Hierarchy of urban road networks from a scaling-law perspective — Cities", "https://www.sciencedirect.com/science/article/abs/pii/S0264275125005165"),
 ],
},
{
 "n": 20,
 "slug": "congestion-is-variability",
 "field": "Urban Systems & Cities",
 "kind": "Traffic theory",
 "title": "Congestion Onset Is Variability, Not Volume",
 "claim": "The onset of urban congestion is set by the variability of demand rather than its mean, so two roads carrying the same average flow can be free-flowing or gridlocked depending on the variance, and smoothing demand is worth more than adding capacity.",
 "dek": "Traffic models add lanes. The queue is often made by the peaks, not the average.",
 "evidence": [
   ("Overflow queue models for signalised urban intersections treat congestion as a queueing problem in which variability of arrivals, not just mean flow, determines whether queues clear between cycles.",
    "https://trid.trb.org/view/843845", "Transportation Research Board"),
   ("Work on congestion in networks has described the onset as a phase transition driven by load relative to capacity, which is the structure a variability-controlled threshold requires.",
    "https://www.stat.berkeley.edu/~aldous/Research/OP/congestion.html", "Berkeley, congestion phase transitions"),
 ],
 "tension": "Capacity planning is based on average daily traffic, with a peak-hour factor applied as a correction. The correction is a fixed multiplier, which assumes variability is a constant fraction of the mean. It is not, and the residual shows up as unpredicted gridlock on roads that are nominally under capacity.",
 "theory": [
   "A road is a queueing system. Whether a queue clears depends on whether arrivals in a cycle exceed the capacity to discharge them. A mean flow below capacity can still produce a growing queue if the variance is high enough, because the exceedances are what accumulate.",
   "That makes the onset a function of the ratio of variance to spare capacity rather than of the mean. Two roads with the same average can be stable or unstable depending on the variance, and the transition is sharp because queues grow without bound once arrivals persistently exceed discharge.",
   "The prediction is that congestion onset should be predicted by demand variability better than by mean volume-to-capacity ratio, and that smoothing demand, through pricing or scheduling, should delay onset more than adding capacity of the same cost.",
   "This changes the economics of congestion policy: capacity is a high-variance fix, while demand smoothing targets the variable that actually controls the transition."
 ],
 "prior": ("Volume-to-capacity ratio planning",
           "The ratio compares mean flow to capacity and treats variability as a peak factor. This makes variability the controlling variable and predicts that the same ratio can be stable or unstable."),
 "predictions": [
   ("P1", "Congestion onset should be predicted by demand variability better than by mean volume-to-capacity ratio."),
   ("P2", "Roads matched on mean flow but differing in variance should differ in breakdown probability."),
   ("P3", "Demand smoothing should delay onset more than equivalent-cost capacity additions."),
 ],
 "refute": "Onset tracking the mean ratio regardless of variance would restore the capacity account.",
 "change": "It would shift congestion policy from building to smoothing, and change how road schemes are appraised.",
 "sources": [
   ("Modeling overflow queues on urban signalized intersections — TRB", "https://trid.trb.org/view/843845"),
   ("Congestion in networks: phase transitions — Berkeley", "https://www.stat.berkeley.edu/~aldous/Research/OP/congestion.html"),
 ],
},
{
 "n": 21,
 "slug": "surface-sets-fatigue-life",
 "field": "Manufacturing & Metrology",
 "kind": "Manufacturing science",
 "title": "The Surface Outvotes the Alloy",
 "claim": "Fatigue life of a machined component is set more by surface roughness than by bulk material properties, so two parts from the same alloy can differ in life by an order of magnitude, and surface specification should dominate alloy selection.",
 "dek": "Design teams argue about the alloy. The crack starts in the last ten micrometres.",
 "evidence": [
   ("Surface roughness and a non-martensitic surface layer were shown to affect fatigue life, with the surface condition controlling initiation rather than the bulk.",
    "https://www.jstage.jst.go.jp/article/jjsem/11/Special_Issue/11_Special_Issue_s251/_article", "JSEM"),
   ("A gradient-based multiaxial criterion for fatigue crack initiation has been developed for components with surface roughness, which means the roughness effect can be predicted rather than only observed.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0142112318303943", "International Journal of Fatigue"),
 ],
 "tension": "Material selection is driven by bulk properties: strength, toughness, corrosion resistance. Surface finish is specified as a manufacturing detail, often with a single roughness number and little attention to its direction or the subsurface layer it leaves. Failures then occur at stresses the bulk data said were safe.",
 "theory": [
   "Fatigue cracks initiate at stress concentrations. A rough surface is a field of them, with each groove acting as a small notch. The local stress at the root of a groove can exceed the nominal stress by a factor that depends on the groove geometry, not on the alloy.",
   "If initiation dominates life, then the surface is the controlling variable. That predicts that a rough part in a strong alloy can fail before a smooth part in a weaker one, and that the ranking of alloys by fatigue life should collapse when roughness is controlled.",
   "The prediction is measurable with existing test machines: hold the alloy fixed and vary roughness, and the life should span an order of magnitude. Then hold roughness fixed and vary the alloy, and the span should be much smaller.",
   "The manufacturing consequence is that surface specification should be treated as a design variable with the same status as alloy choice, and that finishing processes should be selected for the residual stress they leave as well as the roughness they produce."
 ],
 "prior": ("Bulk-property fatigue design",
           "Bulk design uses S-N curves from polished specimens and applies a surface factor. This makes the surface the primary variable and the alloy secondary, which predicts that alloy ranking is unstable across finishes."),
 "predictions": [
   ("P1", "Fatigue life should vary more with roughness than with alloy at fixed geometry."),
   ("P2", "Alloy ranking by fatigue life should collapse when roughness is controlled."),
   ("P3", "Surface residual stress should predict life better than bulk strength."),
 ],
 "refute": "Life tracking bulk strength at controlled roughness would restore the material-first account.",
 "change": "It would move surface specification into design, and change how finishing processes are selected.",
 "sources": [
   ("Effects of surface roughness and surface layer on fatigue life — JSEM", "https://www.jstage.jst.go.jp/article/jjsem/11/Special_Issue/11_Special_Issue_s251/_article"),
   ("Gradient-based multiaxial criterion for fatigue initiation with surface roughness — IJF", "https://www.sciencedirect.com/science/article/abs/pii/S0142112318303943"),
 ],
},
{
 "n": 22,
 "slug": "porosity-is-melt-pool-instability",
 "field": "Manufacturing & Metrology",
 "kind": "Additive manufacturing",
 "title": "Porosity Comes From the Pool, Not the Powder",
 "claim": "Porosity in metal additive manufacturing is generated by oscillatory instabilities in the melt pool rather than by powder quality or energy density, so defect density should track pool oscillation frequency and be controllable by modulation rather than by tighter powder specifications.",
 "dek": "Quality control blames the powder. The defect may be born in the puddle.",
 "evidence": [
   ("Melt pool dynamics and pore formation in selective laser melting have been characterised mechanistically, with pore formation tied to pool behaviour rather than to feedstock variability alone.",
    "https://www.sciencedirect.com/science/article/pii/S0264127525004630", "Materials and Design"),
   ("Mixing-driven defects and composition evolution in multi-material additive manufacturing show that pool-level transport, not powder quality, can dominate defect generation.",
    "https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.75723", "Advanced Science"),
 ],
 "tension": "Powder quality and energy density are the standard levers, and both matter. But defect density varies between machines and between builds with the same powder and the same nominal parameters, and the variation tracks the pool's behaviour rather than the feedstock. That residual is usually treated as machine-specific noise.",
 "theory": [
   "A laser melt pool is a driven fluid with a free surface. Under certain conditions it oscillates, and the oscillation can pinch off and trap vapour or eject material, leaving a pore. The instability is a property of the pool, not of the powder.",
   "If porosity is instability-driven, then defect density should correlate with the oscillation frequency and amplitude rather than with powder size distribution, and it should be suppressible by modulating the laser at a frequency that damps the oscillation.",
   "The prediction is that a modulated build should have lower porosity at the same energy density, and that in-situ monitoring of pool emission should predict porosity before the layer is complete.",
   "This changes the economics of additive quality: powder specification is expensive, while modulation is a firmware change, and if the theory is right the cheaper lever is also the effective one."
 ],
 "prior": ("Powder-quality and energy-density models",
           "Those models attribute porosity to feedstock or to insufficient energy. This attributes it to pool instability, which predicts that identical powder can give different porosity and that modulation can fix it."),
 "predictions": [
   ("P1", "Defect density should correlate with pool oscillation metrics better than with powder size distribution."),
   ("P2", "Laser modulation at a damping frequency should reduce porosity at constant energy density."),
   ("P3", "In-situ pool monitoring should predict porosity before layer completion."),
 ],
 "refute": "Porosity tracking powder quality at controlled pool dynamics would restore the feedstock account.",
 "change": "It would replace powder specification with process modulation, and make in-situ monitoring a quality record.",
 "sources": [
   ("Melt pool dynamics and pore formation in selective laser melting — Materials and Design", "https://www.sciencedirect.com/science/article/pii/S0264127525004630"),
   ("Mixing-driven defects in multi-material metal additive manufacturing — Advanced Science", "https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.75723"),
 ],
},
{
 "n": 23,
 "slug": "vulnerability-density-is-churn",
 "field": "Software Security",
 "kind": "Empirical security",
 "title": "Vulnerabilities Follow Code Churn",
 "claim": "Vulnerability density in a codebase is set by the rate of change of the code rather than by its size or its age, so projects with identical line counts can differ by an order of magnitude in vulnerability rate, and the rate should be predicted from churn before any code review.",
 "dek": "Security review budgets by size. The defects may be where the code is moving.",
 "evidence": [
   ("An empirical study of the security of open source web applications examined vulnerability occurrence against project characteristics, and found that the determinants are properties of the development process rather than of size alone.",
    "https://www.computer.org/csdl/proceedings-article/esem/2009/05314215/12OmNyGtjmg", "ESEM, 2009"),
   ("The same study reports vulnerability data across projects, which is the structure a churn-based predictor requires: a per-project rate that varies more than size alone would suggest.",
    "https://dlnext.acm.org/doi/10.1109/ESEM.2009.5314215", "ACM proceedings record"),
 ],
 "tension": "Security resources are allocated by size and by criticality: larger codebases get more review, and older code is assumed to be more mature. Both assumptions are reasonable and both fail to predict which projects have the most vulnerabilities per line.",
 "theory": [
   "A vulnerability is introduced when code is written or changed. Code that never changes has already been examined, by users and by attackers, so its remaining defects are few. Code that changes constantly is new code, and new code carries new defects.",
   "If churn is the driver, then vulnerability density should scale with change rate per line rather than with line count, and the relation should hold across languages and project ages. Mature projects should look safe not because they are old but because they have stopped moving.",
   "The prediction is that churn measured over the preceding period should predict future vulnerability discovery better than size, age or complexity metrics, and that a project that doubles its change rate should roughly double its rate.",
   "The practical consequence is that review effort should follow churn, which is cheap to measure from version control, rather than following size, which is the current proxy."
 ],
 "prior": ("Size- and age-based review allocation",
           "Those heuristics assume defects scale with lines and decline with age. This makes churn the driver, which predicts that a large stable project is safer than a small volatile one."),
 "predictions": [
   ("P1", "Churn should predict future vulnerability discovery better than size or age."),
   ("P2", "Vulnerability density should scale with change rate per line across projects."),
   ("P3", "Doubling churn should approximately double the discovery rate."),
 ],
 "refute": "Vulnerability rate tracking size after churn is controlled would restore the size heuristic.",
 "change": "It would reallocate security review by change rate, and make version-control history a security signal.",
 "sources": [
   ("Security of open source web applications — ESEM", "https://www.computer.org/csdl/proceedings-article/esem/2009/05314215/12OmNyGtjmg"),
   ("Security of open source web applications — ACM record", "https://dlnext.acm.org/doi/10.1109/ESEM.2009.5314215"),
 ],
},
{
 "n": 24,
 "slug": "exploitability-is-reachability",
 "field": "Software Security",
 "kind": "Risk theory",
 "title": "Severity Is Not Reachability",
 "claim": "The practical risk of a vulnerability is bounded by whether the vulnerable code is reachable in a given deployment rather than by its severity score, so severity-ranked patch lists misallocate effort and reachability analysis should precede scoring.",
 "dek": "A critical bug in code you never call is not critical for you.",
 "evidence": [
   ("Empirical work on web application security shows that vulnerability prevalence and exploitability depend on how components are used in a particular application, not only on the properties of the component in isolation.",
    "https://www.computer.org/csdl/proceedings-article/esem/2009/05314215/12OmNyGtjmg", "ESEM, 2009"),
   ("The measurement problem is structural: severity scores are assigned to components, while risk accrues to deployments, and the two are related by configuration rather than by scoring.",
    "https://dlnext.acm.org/doi/10.1109/ESEM.2009.5314215", "ACM record"),
 ],
 "tension": "Patch management is driven by severity. Teams triage by CVSS and prioritise the highest scores, which is rational given the information available. But most critical-scored vulnerabilities are in unused paths, and the ones that matter are often medium-scored and reachable.",
 "theory": [
   "A vulnerability can only be exploited if an attacker can reach the vulnerable code through the application's actual interfaces. Reachability is a property of the deployment, and it is binary or near-binary: either the path exists or it does not.",
   "That makes risk a product of severity and reachability, and because reachability is often zero, the product is often zero. Ranking by severity alone therefore over-weights unreachable criticals and under-weights reachable moderates.",
   "The prediction is that realised exploits should correlate with reachability better than with severity, and that a patch list ordered by severity-reachability product should outperform one ordered by severity on time-to-fix for exploited vulnerabilities.",
   "The practical consequence is that reachability analysis, which is increasingly automatable, should precede scoring in the triage pipeline, and that severity scores should be reported as conditional on reachability."
 ],
 "prior": ("Severity-based patch prioritisation",
           "Severity ranks by potential impact in the abstract. This ranks by impact given reachability, which predicts that most urgent patches are not the highest-scored ones."),
 "predictions": [
   ("P1", "Realised exploits should correlate with reachability better than with severity."),
   ("P2", "A reachability-weighted list should improve time-to-fix for exploited vulnerabilities."),
   ("P3", "Most critical-severity vulnerabilities should be unreachable in typical deployments."),
 ],
 "refute": "Exploit occurrence tracking severity regardless of reachability would restore the scoring approach.",
 "change": "It would restructure patch triage and make reachability a first-class security metric.",
 "sources": [
   ("Security of open source web applications — ESEM", "https://www.computer.org/csdl/proceedings-article/esem/2009/05314215/12OmNyGtjmg"),
   ("Security of open source web applications — ACM", "https://dlnext.acm.org/doi/10.1109/ESEM.2009.5314215"),
 ],
},
{
 "n": 25,
 "slug": "damage-is-a-rate",
 "field": "Nuclear & Radiation",
 "kind": "Radiation biology",
 "title": "Damage Is a Rate, Not a Dose",
 "claim": "Biological damage from ionising radiation is set by dose rate rather than by total dose, because repair saturates above a threshold rate, so the same total dose delivered slowly can be substantially less harmful than delivered quickly.",
 "dek": "Radiation protection counts grays. The cell counts how fast they arrive.",
 "evidence": [
   ("Dose-rate effectiveness and potentially lethal damage repair have been demonstrated in normal and double-strand-break repair deficient murine cells, which isolates repair as the mechanism behind the rate dependence.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0304383597004400", "Mutation Research"),
   ("The effect of dose rate on residual DNA damage markers and micronucleus frequency has been measured directly, showing that the same dose produces different biological outcomes depending on how it is delivered.",
    "https://europepmc.org/article/MED/25738897", "Europe PMC"),
 ],
 "tension": "Radiation protection is built on dose, with a dose-rate modifier applied as a correction. The modifier is treated as a parameter to be fitted, not as a consequence of a mechanism, which means it cannot be extrapolated to exposure scenarios that were not in the fitting data.",
 "theory": [
   "DNA double-strand breaks are repaired continuously. At low dose rates, repair keeps pace with damage and the fraction of breaks that persist is small. Above a threshold rate, repair saturates and breaks accumulate.",
   "That makes the response a saturating function of rate, with a threshold. Below the threshold, dose is a poor predictor because the same total dose spread over a long time is repaired; above it, dose accumulates almost linearly.",
   "The prediction is a threshold in the rate-response curve rather than a smooth modifier, and it predicts that chronic exposure below the threshold should be disproportionately less harmful than acute exposure of the same total. It also predicts that individuals with impaired repair should have a lower threshold.",
   "This matters for protection standards because a threshold changes the shape of the risk model at low doses, and for medicine because it justifies fractionation explicitly rather than empirically."
 ],
 "prior": ("Linear dose-response with a rate modifier",
           "The linear model makes risk proportional to dose with a fitted modifier. This makes risk a saturating function of rate with a threshold, which predicts a change in shape rather than a scaling factor."),
 "predictions": [
   ("P1", "The rate-response curve should show a threshold rather than a smooth modifier."),
   ("P2", "Chronic exposure below the threshold should be disproportionately less harmful than acute exposure of equal total dose."),
   ("P3", "Repair-deficient individuals should show a lower threshold."),
 ],
 "refute": "Risk proportional to total dose at all rates, with no threshold, would restore the linear model.",
 "change": "It would change low-dose risk extrapolation and make fractionation a mechanistic rather than empirical choice.",
 "sources": [
   ("Dose-rate effectiveness and potentially lethal damage repair in repair-deficient murine cells — Mutation Research", "https://www.sciencedirect.com/science/article/abs/pii/S0304383597004400"),
   ("Effect of dose rate on residual DNA damage and micronuclei — Europe PMC", "https://europepmc.org/article/MED/25738897"),
 ],
},
{
 "n": 26,
 "slug": "risk-is-repair-capacity",
 "field": "Nuclear & Radiation",
 "kind": "Radiation epidemiology",
 "title": "Risk Is Repair Capacity",
 "claim": "Individual radiation cancer risk is set by DNA repair capacity rather than by cumulative dose, so a repair-capacity biomarker should predict risk better than occupational dose records, and dose-based regulation should be supplemented by capacity screening.",
 "dek": "Two workers with the same badge dose can have very different risks. The difference may be measurable.",
 "evidence": [
   ("Comparisons of normal and double-strand-break repair deficient cells show that the biological consequence of a given dose depends on repair capacity, which is the individual-level variation a capacity model requires.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0304383597004400", "Mutation Research"),
   ("Residual DNA damage markers after irradiation vary between individuals at the same dose, which is the measurement a capacity biomarker would formalise.",
    "https://europepmc.org/article/MED/25738897", "Europe PMC"),
 ],
 "tension": "Radiation protection is dose-based and population-averaged. It assumes that the same dose carries the same risk for everyone, which is administratively convenient and biologically implausible. Individual variation is acknowledged and not measured, because there has been no validated assay.",
 "theory": [
   "Cancer risk from radiation depends on the probability that a damaging lesion escapes repair and becomes a mutation. That probability is a function of repair capacity, which varies between individuals for genetic and age-related reasons.",
   "If capacity is the determinant, then dose is a proxy that works on average and fails at the individual level. The prediction is that a repair-capacity assay should stratify risk within a dose band, and that the highest-risk individuals should be identifiable before exposure rather than after.",
   "The theory also predicts an interaction: the effect of dose should be larger in low-capacity individuals, so the dose-response slope should differ by capacity group. That is a testable heterogeneity in existing cohort data if stored samples allow the assay.",
   "The regulatory consequence is uncomfortable but tractable. Dose limits could remain as a universal floor while capacity screening adds an individual layer for high-exposure occupations, which is how other individual susceptibility factors are already handled."
 ],
 "prior": ("Population-averaged dose-risk models",
           "The dose model assumes a single risk per unit dose. This assumes a distribution of susceptibilities, which predicts heterogeneity that the aggregate model cannot see."),
 "predictions": [
   ("P1", "Repair capacity should predict cancer risk within a dose band."),
   ("P2", "The dose-response slope should be steeper in low-capacity individuals."),
   ("P3", "Capacity screening should identify high-risk workers before exposure."),
 ],
 "refute": "No risk heterogeneity by capacity within dose bands would leave the averaged model adequate.",
 "change": "It would add an individual layer to radiation protection and change how occupational screening is designed.",
 "sources": [
   ("Dose-rate effectiveness and repair deficiency — Mutation Research", "https://www.sciencedirect.com/science/article/abs/pii/S0304383597004400"),
   ("Residual DNA damage markers and individual variation — Europe PMC", "https://europepmc.org/article/MED/25738897"),
 ],
},
]