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Measurement Blind Spots · supplied 1 November 2026

Sources, provenance & corrections

Supplied working research · no experiment performed.

Analytical derivations and simulations are source-reported, not independently reproduced during publication. No published dataset was directly reanalysed. Source readiness/novelty scores are author assessments, not validation or novelty clearance. Original claims, corrections and negative results are retained.

Complete originals

All 27 original files · Full SHA-256 inventory · PDF · Word · CSV · JSON

Export completeness

The received export preserves the full compiled document, structured hypotheses, code, available outputs and figures. Some paths mentioned in its prose (separate hyp/ and refined/ directories, several data/ outputs) are not present in this export. We have not invented or regenerated missing files. The 30 refined entries are extracted from the compiled Markdown; all original files retain their bytes.

Corrections and negative findings

Corrections, negative results, and superseded claims

This file records everything in the collection that changed during construction, every claim that failed a quantitative check, and every place where a hypothesis was narrowed rather than defended. It is deliberately kept separate from the hypotheses so that a reader can see what the evidence did to the original idea.

1. Two hypotheses were replaced before publication

C38 (chemistry). The original entry claimed that electrochemical ammonia synthesis rates near the detection limit are dominated by ambient and membrane-derived contamination. A check against the prior Incoprea corpus found an existing hypothesis making essentially the same claim (“Most reported electrochemical ammonia synthesis is contamination”). Rather than republish it with a new number, C38 was replaced with a different electrochemistry hypothesis: that scanning electrochemical microscopy assigns a finite rate constant to insulating regions because its feedback theory assumes a conductive substrate. The replacement has a different mechanism (diffusion-limited approach curve fitted with a conducting-substrate model), a different observable (tip-radius dependence of the apparent rate over a known insulator), and a different refutation.

P20 (physics). The original entry attributed the residual muon g−2 discrepancy to a scheme mismatch in the intermediate window between lattice and data. A prior Incoprea hypothesis already assigns the residual to lattice hadronic-vacuum-polarization systematics. The overlap was judged too close. P20 was replaced with a different precision-physics hypothesis: that optical-clock comparisons inherit an unmodelled self-gravity term from the apparatus mass. The replacement is testable with a ballast-variation experiment and does not depend on the g−2 literature.

2. A hypothesis failed its own quantitative check: R04 / B01

Original claim. Plate-reader OD600 compresses above ~0.3–0.6 because of detector saturation and stray light, suppressing fitted growth rates.

What was computed. A stray-light model, measured OD = −log₁₀(10^(−OD_true) + S), with the bias in fitted μ obtained from a log–log regression over four candidate windows (R1_refinement_numbers.py, section A).

Result. - S = 10⁻³, window OD 0.2–1.0: bias −0.1% - S = 10⁻², window OD 0.2–1.0: bias −1.1% - S = 10⁻³, window OD 2.0–3.0: bias −8.0% - S = 10⁻², window OD 2.0–3.0: bias −23.4%

Conclusion. Stray light cannot explain compression in the OD 0.3–0.6 range. The hypothesis as originally stated is not supported. It was narrowed: the mechanism is real but confined to OD > 1.5, and any sub-exponential growth below OD 1 must have a different cause. The entry in hyp/04-biology.md now carries the corrected numbers and names the surviving candidate mechanisms (detector nonlinearity, meniscus and fill volume, evaporation, physiological shift) along with the tests that separate them.

Why it is kept. A hypothesis that loses cleanly is the point of the collection. The failed mechanism was removed from the suspect list for the low-OD regime, which is a real gain.

3. A hypothesis was corrected in direction but not in kind: R21 / X33

Original claim. Inverse-variance weighting with variances estimated from the same data biases the weighted mean toward points whose noise fluctuated low.

What was computed. 40 000 Monte Carlo trials with four measurements and 2–30 degrees of freedom per variance estimate.

Result. The mean is unbiased (−0.000 to 0.005). The spread of the estimator exceeds its nominal value: 0.686 at 2 dof, 0.582 at 5, 0.539 at 10, 0.511 at 30, against a nominal 0.5. The quoted uncertainty is therefore 73%, 86%, 93% and 98% of the true spread.

Conclusion. The heading was wrong, the phenomenon is real. The refined entry now claims uncertainty understatement rather than mean bias, with the dof-dependent magnitude.

4. A hypothesis overstated its own effect size: R20 / X13

Original claim. Non-stationarity makes every estimated autocorrelation time too short, and hence every error bar too small, by a large factor.

What was computed. For a linear trend of amplitude a against noise σ, the ratio of the true standard error to the naive one is √(a²/12 + σ²)/σ.

Result. 1.01 at a/σ = 0.5; 1.04 at 1; 1.15 at 2; 1.53 at 4.

Conclusion. The effect is real but modest — under a factor of two even when the trend is four times the noise. The refined entry says so explicitly and restricts the claim to regimes where the trend dominates.

5. A claim about test power was softened after computation

In I1 the initial framing assumed that a broad distribution of relaxation times would be indistinguishable from a power law over a short window. Direct evaluation of the log-normal Laplace transform showed the opposite: a log-normal spectrum produces a strongly curved decay, and a 0.5-decade window is enough to reject a single power law at 1% noise when the window sits on the spectral knee. The investigation was rebuilt around the offset term instead, where the degeneracy is genuine and the Cramér–Rao bound actually diverges. The discarded framing is recorded here rather than deleted.

6. Numerical errors caught during development

7. Things that were not done

Methodology and source-assessment depth

Methodology, scales, and limits

What was built

  1. A source base. A programmatic harvest of 817 arXiv records submitted between January 2024 and December 2026, drawn from 49 queries aimed at measurement systematics rather than at subject matter: “systematic error”, “measurement bias”, “unobservable”, “blind spot”, “cannot be distinguished”, “degeneracy”, “identifiability”, “hidden variable”, “nuisance parameter”, “model discrepancy”, “interlaboratory”, “boundary condition”, “contact resistance”, “nonlinear impedance”, “third harmonic”, “small-signal approximation”, “Kramers–Kronig”, “instrument response”, “deconvolution”, “drift”, “aging”, “hysteresis”, “model misspecification”, and 26 others. The harvester is src/harvest_arxiv.py; the raw pack is data/arxiv_pack.json.
  2. A prior-art screen. 358 unique prior Incoprea hypothesis titles were extracted from the existing export bundles, and every new title was compared against them by string similarity. Two substantive overlaps were found and replaced (see CORRECTIONS.md).
  3. Hypothesis construction. 300 hypotheses were written directly, in six domain batches: physics and astronomy (50), chemistry and electrochemistry (50), materials (50), biology and medicine (50), engineering and Earth science (50), cross-cutting methodology (50). Each was required to have a named baseline, a mechanism, a discriminating observation, an available test, prior art, and an explicit refutation. The parser src/parse_hyp.py enforces the schema and rejects missing fields.
  4. Selection and refinement. 30 were selected for development on three criteria: the mechanism had to be expressible quantitatively; the blind spot had to be invisible to the instrument or test that is normally used as a check; and the refutation had to be cheap. Refinement required labelling each part of the claim as an identity, an artefact, or a genuine prediction.
  5. Investigation. Three were taken to full mathematical treatment with validated code: finite-window decay identifiability (I1), spot-size bias in thermal metrology (I2), and Kramers–Kronig blindness (I3). Every number quoted in the refined set is regenerated by investigations/R1_refinement_numbers.py.

What “readiness” means

Score Meaning
5 A reanalysis of published data, or a two-point change to an existing measurement, would settle it
4 Existing public data or an existing apparatus can confirm or kill it
3 Needs a dedicated measurement campaign or a new analysis pipeline
2 Needs new instrumentation
1 Needs the idea made precise before it needs an experiment

Readiness is a judgement about cost, not importance. Of the 300: 45 are scored 5, 201 are scored 4, and 54 are scored 3.

What “novelty” means

Score Meaning
5 A mechanism not present in the literature as far as the source base shows, with a new observable
4 A known mechanism applied where it is not currently applied, with a quantitative prediction
3 A known mechanism given a specific, testable form in a new domain
2 A known effect quantified for a domain that has not quantified it
1 A restatement or a small extension

Novelty 4–5 is claimed for 61 hypotheses; the rest are mostly 3. This is deliberate: most real progress in measurement science comes from taking a known mechanism seriously in a place it has not been taken seriously, not from inventing a new mechanism.

The three labels used in the refined set

Limits

Reproducing the collection

cd blindspot-2026-11-01
../.venv-research/bin/python src/parse_hyp.py            # rebuild CSV/JSON, validate schema
../.venv-research/bin/python investigations/I1_offset_window_bias.py
../.venv-research/bin/python investigations/I2_boundary_bias.py
../.venv-research/bin/python investigations/I3_kk_blindspot.py
../.venv-research/bin/python investigations/R1_refinement_numbers.py
python3 build/build_doc.py                                # assemble and typeset

The arXiv harvest is not required to rebuild the document; data/arxiv_pack.json is included. Re-running src/harvest_arxiv.py will produce a different pack because arXiv grows, so the pack shipped here is the one this collection was built from.

File inventory

Bibliographic identifiers and HTTP checks are source-reported. Abstract-level reading is not a full-text assessment of support for the exact hypothesis. Novelty, outside review and physical validation remain separate questions.