# Volume Seven — theories 7-12 (new file)
THEORIES = [
{
 "n": 7,
 "slug": "endurance-is-heat-dissipation",
 "field": "Sports & Exercise Physiology",
 "kind": "Exercise physiology",
 "title": "Endurance Ends When Heat Cannot Leave",
 "claim": "Endurance performance is limited by heat dissipation rather than by oxygen delivery, so the limiting factor should change with ambient conditions, and cooling interventions should improve performance more than oxygen supplementation in the heat.",
 "dek": "The limit is described as a fuel line. In the heat it may be a radiator.",
 "evidence": [
   ("Exercise in the heat has been reviewed as a thermoregulatory limitation to performance in humans and horses, with the constraint located in heat loss rather than in oxygen supply.",
    "https://pubmed.ncbi.nlm.nih.gov/10198141/", "PubMed review"),
   ("Work near and above the severe intensity domain has been analysed with skin blood flow and heat offloading as the mechanism, which places the limit in the environment-facing side of the system.",
    "https://europepmc.org/article/MED/35347469", "Europe PMC"),
 ],
 "tension": "Endurance is modelled with a maximal oxygen uptake and a lactate threshold, and performance is predicted from those. The model works in temperate conditions and fails in the heat, where the same oxygen uptake produces a much shorter time to exhaustion, and the failure is attributed to dehydration rather than to the physical constraint.",
 "theory": [
   "Muscular work produces heat that must be carried to the skin and lost to the environment. The rate of loss is set by skin blood flow, by the temperature gradient, and by evaporation, all of which are finite.",
   "When the required loss approaches the maximum, the body must choose between delivering blood to muscle and delivering it to skin. The choice is what limits performance, and it is a physical constraint rather than a fuel one.",
   "The prediction is that the limiting factor should shift with ambient conditions: in the cold, oxygen delivery should predict performance; in the heat, heat loss capacity should. Cooling should improve performance more than oxygen in the heat, and the reverse in the cold.",
   "The practical consequence is that performance prediction should include an environmental term, and that cooling strategies should be prescribed by conditions rather than used as a generic aid."
 ],
 "prior": ("Oxygen-delivery models of endurance",
           "Those models make performance a function of oxygen kinetics. This makes it a function of the environment in the heat, which explains the collapse of prediction in hot conditions."),
 "predictions": [
   ("P1", "The limiting factor should shift from oxygen to heat as ambient temperature rises."),
   ("P2", "Cooling should outperform oxygen supplementation in the heat."),
   ("P3", "Performance should be predictable from heat-loss capacity in hot conditions."),
 ],
 "refute": "Performance tracking oxygen uptake across environments would leave the oxygen model adequate.",
 "change": "It would change how endurance is tested and how cooling is prescribed.",
 "sources": [
   ("Exercise in the heat: thermoregulatory limitations to performance — PubMed", "https://pubmed.ncbi.nlm.nih.gov/10198141/"),
   ("Environmental stimuli and work above the critical power boundary — Europe PMC", "https://europepmc.org/article/MED/35347469"),
 ],
},
{
 "n": 8,
 "slug": "tendon-stiffness-precedes-strength",
 "field": "Sports & Exercise Physiology",
 "kind": "Musculoskeletal adaptation",
 "title": "Tendon Stiffness Comes First",
 "claim": "Strength gains from training are set by tendon stiffness rather than by muscle size, so force should increase before hypertrophy is measurable, and training that stiffens tendon should produce earlier gains than training that targets muscle alone.",
 "dek": "Strength is measured in muscle. The force has to get through a spring to reach the bone.",
 "evidence": [
   ("Athletic training has been shown to affect the uniformity of muscle and tendon adaptation, with tendon properties changing alongside muscle and not always in step with them.",
    "https://journals.physiology.org/doi/pdf/10.1152/japplphysiol.00493.2016", "Journal of Applied Physiology"),
   ("Musculoskeletal adaptations to resistance training have been documented across muscle and tendon, with the tendon adapting on a different timescale from the muscle.",
    "https://www.sciencedirect.com/science/article/abs/pii/S1356689X0600052X", "Manual Therapy"),
 ],
 "tension": "Strength is attributed to muscle cross-sectional area, and training programmes are designed around hypertrophy. Early strength gains occur before any measurable change in muscle size, and the usual explanation is neural, which is a label rather than a measurement.",
 "theory": [
   "Force generated by muscle is transmitted through tendon to bone. If the tendon stretches, part of the muscle's shortening is lost and the rate of force development falls, so the effective strength at the bone depends on tendon stiffness.",
   "Training stiffens tendon over weeks, faster than muscle grows. That makes stiffness a plausible mechanism for early gains, and it predicts that force should increase before cross-sectional area does.",
   "The prediction is that tendon stiffness should predict strength gains better than muscle size in the first weeks, and that training which loads tendon should produce earlier gains than training which does not.",
   "The practical consequence is that strength programmes should include tendinous loading, and that strength testing should include rate of force development rather than peak force alone."
 ],
 "prior": ("Hypertrophy-led strength models",
           "Those models make strength a function of muscle size. This makes early strength a function of tendon stiffness, which explains gains that precede growth."),
 "predictions": [
   ("P1", "Tendon stiffness should predict early strength gains better than muscle size."),
   ("P2", "Force should rise before cross-sectional area changes."),
   ("P3", "Tendon-loading programmes should produce earlier gains."),
 ],
 "refute": "Early gains tracking hypertrophy at controlled tendon stiffness would leave the muscle account adequate.",
 "change": "It would change strength programme design and testing.",
 "sources": [
   ("Athletic training and the uniformity of muscle and tendon adaptation — J Appl Physiol", "https://journals.physiology.org/doi/pdf/10.1152/japplphysiol.00493.2016"),
   ("Musculoskeletal adaptations to resistance training — Manual Therapy", "https://www.sciencedirect.com/science/article/abs/pii/S1356689X0600052X"),
 ],
},
{
 "n": 9,
 "slug": "fatigue-is-a-prediction",
 "field": "Sports & Exercise Physiology",
 "kind": "Fatigue theory",
 "title": "Fatigue Is a Prediction, Not a Governor",
 "claim": "Fatigue is the output of a predictive model of remaining capacity rather than a central governor that protects the body, so it should be updated by unexpected evidence and should be manipulable by information at fixed physiology.",
 "dek": "The brain is said to hold a governor. It may be running a forecast.",
 "evidence": [
   ("The central governor model has been critically reviewed, with the review noting that the model explains the phenomenology but lacks a specified mechanism and makes predictions that are hard to falsify.",
    "https://bjsm.bmj.com/content/40/7/573.short", "British Journal of Sports Medicine"),
   ("The critique identifies the gap that a predictive account would fill: a mechanism that generates the pacing decision from available information rather than a homunculus that limits output.",
    "https://scholar.google.si/citations?view_op=view_citation&hl=en&user=GZCp8PoAAAAJ&citation_for_view=GZCp8PoAAAAJ:O3NaXMp0MMsC", "CGM critique"),
 ],
 "tension": "Fatigue is explained either peripherally, by metabolite accumulation, or centrally, by a governor. The peripheral account fails because fatigue can be reversed by motivation, and the governor account fails because it names an agent without describing what it computes.",
 "theory": [
   "The brain continuously forecasts how long the current effort can be sustained, using prior experience, afferent feedback and the expected task duration. The forecast, not the metabolite concentration, sets the chosen output.",
   "A forecast can be wrong, and it can be updated by information. That predicts that misleading information about the remaining distance should change output at fixed physiology, and that unexpected progress should increase it.",
   "The prediction is that pacing should be predicted by the forecast error rather than by any physiological variable, and that interventions which change the forecast should change performance without changing the body.",
   "The practical consequence is that pacing strategy becomes an information problem, and that fatigue in clinical populations could be assessed as a forecasting deficit rather than as a metabolic one."
 ],
 "prior": ("The central governor model",
           "The governor names a limiter without specifying its computation. This specifies a forecast, which makes falsifiable predictions about information effects at fixed physiology."),
 "predictions": [
   ("P1", "Misleading distance information should change output at fixed physiology."),
   ("P2", "Pacing should be predicted by forecast error rather than by metabolites."),
   ("P3", "Unexpected progress should increase output."),
 ],
 "refute": "Output invariant to information at fixed physiology would leave the governor's implicit determinism intact.",
 "change": "It would make pacing an information problem and change how fatigue is assessed clinically.",
 "sources": [
   ("Is fatigue all in your head? A critical review of the central governor model — BJSM", "https://bjsm.bmj.com/content/40/7/573.short"),
   ("Central governor model — critical review record", "https://scholar.google.si/citations?view_op=view_citation&hl=en&user=GZCp8PoAAAAJ&citation_for_view=GZCp8PoAAAAJ:O3NaXMp0MMsC"),
 ],
},
{
 "n": 10,
 "slug": "flavour-is-a-trajectory",
 "field": "Food Science & Fermentation",
 "kind": "Fermentation science",
 "title": "Flavour Is a Trajectory, Not a Recipe",
 "claim": "The flavour of a fermented food is set by the sequence of microbial stages rather than by the final composition, so two fermentations ending with the same species and metabolites can taste different, and process control should target the trajectory.",
 "dek": "Two batches end in the same place and taste different. The difference is the route.",
 "evidence": [
   ("Flavour formation in a fermented food has been characterised through a three-stage model, with the mechanism of flavour development tied to the succession rather than to the endpoint.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0308814625045686", "Food Chemistry"),
   ("The three-stage account implies that intermediate metabolites produced early persist into the final product even when the organisms that made them are no longer dominant.",
    "https://www.sciencedirect.com/science/article/abs/pii/S0308814625045686", "Succession and flavour"),
 ],
 "tension": "Fermentation is controlled by endpoint specifications: final pH, final species composition, final metabolite concentrations. Those specifications are met by batches that taste different, and the difference is attributed to terroir or to chance rather than to the sequence.",
 "theory": [
   "A fermentation is a succession. Each stage produces metabolites that the next stage may consume, convert or leave behind. The final profile therefore depends on which intermediates were produced and how long they persisted.",
   "If the trajectory matters, then two batches with identical endpoints can differ because their intermediate histories differ, and the difference is in trace compounds that the endpoint specification does not include.",
   "The prediction is that flavour should be predicted by the trajectory, measured as the sequence of dominant genera and metabolites, better than by the final composition. It also predicts that interventions targeting an early stage should change flavour even when the endpoint is unchanged.",
   "The practical consequence is that process control should monitor the trajectory and that specifications should include intermediate markers rather than only endpoints."
 ],
 "prior": ("Endpoint-specification models",
           "Endpoint models judge a fermentation by where it finishes. This judges it by how it got there, which explains flavour differences between identical endpoints."),
 "predictions": [
   ("P1", "Flavour should be predicted by trajectory better than by final composition."),
   ("P2", "Early-stage interventions should change flavour at constant endpoint."),
   ("P3", "Intermediate markers should improve specification."),
 ],
 "refute": "Flavour determined entirely by endpoint composition would leave the trajectory redundant.",
 "change": "It would change fermentation control and specification.",
 "sources": [
   ("Mechanism of flavour formation in a three-stage fermentation model — Food Chemistry", "https://www.sciencedirect.com/science/article/abs/pii/S0308814625045686"),
 ],
},
{
 "n": 11,
 "slug": "shelf-life-is-water-activity",
 "field": "Food Science & Fermentation",
 "kind": "Food safety",
 "title": "Shelf Life Is a Water Threshold",
 "claim": "Microbial spoilage is governed by water activity rather than by microbial load, so two foods with the same contamination can differ in shelf life by months, and formulation should target water activity rather than preservative concentration.",
 "dek": "Shelf life is estimated from counts. The organisms may be unable to grow regardless of how many are present.",
 "evidence": [
   ("International food hygiene guidance treats water activity as a hurdle that determines whether microorganisms can grow, which is the threshold structure a water-activity account requires.",
    "https://www.fao.org/4/AC334E/AC334E02.htm", "FAO/WHO Codex guidance"),
   ("The hurdle concept formalises shelf life as the product of several inhibitory factors, of which water activity is the one that can be set by formulation alone.",
    "https://www.fao.org/4/AC334E/AC334E02.htm", "Hurdle technology"),
 ],
 "tension": "Shelf life is estimated from initial microbial counts and from challenge testing, and preservatives are added to reduce growth. Counts predict spoilage poorly, because the same count in a dry food and a moist food has completely different consequences.",
 "theory": [
   "Microorganisms need available water, not total water. Below a critical water activity, growth stops regardless of the number of cells present, because the cells cannot maintain turgor and metabolism.",
   "That makes water activity the controlling variable. A food with a high count but low water activity will not spoil, while a food with a low count and high water activity will, and the difference is set by formulation rather than by hygiene.",
   "The prediction is that shelf life should be predicted by water activity better than by initial counts, and that reformulating to lower water activity should extend shelf life without additional preservative.",
   "The practical consequence is that shelf-life prediction should be a water-activity calculation, and that preservatives are a second lever rather than the first."
 ],
 "prior": ("Count-and-preservative models",
           "Those models make spoilage a function of contamination and inhibition. This makes it a function of available water, which explains why counts fail to predict shelf life across formulations."),
 "predictions": [
   ("P1", "Shelf life should correlate with water activity better than with initial counts."),
   ("P2", "Lowering water activity should extend shelf life without added preservative."),
   ("P3", "High-count low-activity foods should be stable."),
 ],
 "refute": "Shelf life tracking counts at controlled water activity would leave the count model adequate.",
 "change": "It would make shelf-life prediction a formulation calculation.",
 "sources": [
   ("Codex Committee on Food Hygiene — hurdles and water activity", "https://www.fao.org/4/AC334E/AC334E02.htm"),
 ],
},
{
 "n": 12,
 "slug": "the-dominant-genus-sets-flavour",
 "field": "Food Science & Fermentation",
 "kind": "Microbial ecology",
 "title": "The Winner Sets the Flavour",
 "claim": "The flavour of a spontaneous fermentation is set by which genus becomes dominant, and the winner is determined by the initial pH rather than by the diversity of the starting community, so flavour is predictable from a single early measurement.",
 "dek": "Complex starters are prized for their diversity. The outcome may be decided by whoever wins the first day.",
 "evidence": [
   ("Fermentation has been regulated on the basis of the dominant genus, with the dominant organism shown to drive microbial succession and the resulting flavour compounds.",
    "https://www.sciencedirect.com/science/article/abs/pii/S221242922500255X", "Food Bioscience"),
   ("The study demonstrates that a single dominant genus can steer the succession, which is the mechanism a winner-takes-all account requires.",
    "https://www.sciencedirect.com/science/article/abs/pii/S221242922500255X", "Dominant genus regulation"),
 ],
 "tension": "Spontaneous fermentations are valued for their microbial diversity, and the assumption is that diversity produces complexity. In practice, most spontaneous fermentations are dominated by one or two genera, and the diversity that remains is often metabolically silent.",
 "theory": [
   "A fermentation is a competition for a limited resource under conditions that change. The organism that grows fastest under the initial conditions establishes a population advantage, and its metabolites then set the environment that selects against competitors.",
   "That makes the winner a function of the starting conditions, particularly pH. A small difference in initial pH can change which genus wins, and therefore the flavour, even with an identical starting community.",
   "The prediction is that initial pH should predict the dominant genus and the flavour profile better than initial diversity, and that adjusting pH should reproducibly steer flavour without changing the starter.",
   "The practical consequence is that fermentation control should target initial conditions rather than inoculum complexity, which is both cheaper and more reproducible."
 ],
 "prior": ("Diversity-produces-complexity models",
           "Those models assume that more starting diversity yields richer flavour. This assumes a winner whose identity sets the flavour, which predicts that initial pH beats diversity as a predictor."),
 "predictions": [
   ("P1", "Initial pH should predict the dominant genus better than initial diversity."),
   ("P2", "Adjusting pH should steer flavour reproducibly."),
   ("P3", "Diversity should not predict flavour once the dominant genus is known."),
 ],
 "refute": "Flavour tracking diversity at fixed initial pH would restore the complexity model.",
 "change": "It would make fermentation control a pH problem rather than an inoculation problem.",
 "sources": [
   ("Regulating fermentation by dominant genus and its effect on succession and flavour — Food Bioscience", "https://www.sciencedirect.com/science/article/abs/pii/S221242922500255X"),
 ],
},
]