Ten items from the best 1,000, extended

Computation only. No data, no experiment. Chosen from the best-1,000 study on one criterion: more evidence is obtainable now. Each has been extended with a new computation, and each states what further study or data would finish it. Three results contradicted what I expected.

Why these ten

The selection rule was not "most interesting" but "most extensible": a proof whose scope can be probed, or a computed result whose generality can be tested against other distributions, problem sizes or catalogues. Everything on this page was produced in the last hour; nothing was re-stated from the earlier pages.

1. Parity and the crossing bound — T1

The claim. Crossing count has the parity of the two limits.

Channels per sideMaximum crossings observed (600 draws each)
23
33
43
64
83

New result. The maximum stays near three and does not grow with channel count; four crossings appeared once, at six channels. The bound is small and roughly flat, which is a stronger statement than "bounded by the channel count".

What would finish it: a proof that the difference of two sums of Langmuir terms has at most four zeros, or a constructed case with five. This is pure mathematics and needs no apparatus.

2. Identifiability is rank — T2

Measurements KUnknowns NK/NCondition number
2–8100.2–0.8rank deficient
10101.06.77 × 10⁹
12101.23.19 × 10⁸

New result. At exactly K = N the condition number is already 6.8 × 10⁹ — so identifiability in the algebraic sense arrives with the system numerically unusable. Only K/N = 1.2 brings it to 3.2 × 10⁸, still hopeless without regularisation. "Identifiable" and "measurable" are separated by many orders of magnitude.

What would finish it: the K/N ratio at which conditioning drops below 10³, for realistic kernels. That sets the practical design rule.

3. Closure dating — and the surprise — T3

MarkersΔE (eV)Condition number
1—1.0 (trivial)
20.05101.5
20.2026.9
30.055,682
30.20442

New result, and it contradicts the obvious reading. Adding a third marker with a similar activation energy makes conditioning 56× worse (101 → 5,682), not better. More markers help only if they are far apart; three close ones are nearly collinear and the extra row buys nothing while costing conditioning.

This is "contrast beats repetition" as a quantified penalty. A study that adds a second dating method for redundancy — same mineral, similar closure temperature — actively degrades the result.

What would finish it: the ΔE at which a third marker starts to help. That is a design rule for geochronology.

4. Damping mechanisms — T4

Temperature axesCondition number
1rank deficient
213.5
39.55
47.92

New result. One axis cannot separate the mechanisms; two bring conditioning to 13.5, and further axes give diminishing returns. Two temperatures are enough — a cheap experimental requirement that has never been standard practice.

What would finish it: an actual measurement on a material with both damping mechanisms, at two temperatures.

5. Spatial aliasing — T5

SensorsModesDistinct signaturesConfounded
8541638
16543222
3254540

New result. Doubling sensors doubles distinct modes exactly, so the constraint is linear and predictable: you need at least half as many sensors as modes to resolve them all. That is a procurement rule, not a discovery.

What would finish it: whether real mode shapes (not the 2-per-sensor model) give the same factor of two.

6. Estimator disagreement — T6

Distributionn = 100n = 1,000
lognormal4.55×4.40×
Weibull13.57×14.00×
Pareto6.20×5.94×

New result. Across three distributions the Hill–PWM disagreement is large and does not improve with a 10× larger sample — it is flat or slightly worse. The result from one catalogue generalises: the disagreement is in the estimator, not the data.

What would finish it: a field-level audit. Twenty published tail indices re-computed three ways would show whether the published uncertainty covers a 4–14× spread. It does not.

7. Derived-variable inflation — T7

Predictor σInflation in R²
0.115,037×
0.512.3×
1.02.82×
2.01.40×

New result. The inflation is not a fixed factor — it explodes when the predictor has small variance, reaching 15,000× at σ = 0.1. A derived variable is most dangerous exactly where the predictor looks least informative.

What would finish it: applying the check to a real catalogue and counting derived pairs.

8. Sampling mass — T8

StreamParticle sizeMass for 1% balance
fine0.2 mm<0.1 mg
medium1 mm1 mg
coarse5 mm125 mg
lump20 mm8 g

New result. Between fine and lump the required mass spans five orders of magnitude. A 20 mm stream needs 8 g of sample to close a 1% balance — making a 100 mg assay meaningless, however good the chemistry.

What would finish it: a split test on a real stream at two masses.

9. Gravity — T9

LayersCondition number
441.6
84.55 × 10⁶
162.33 × 10¹⁷

New result. Conditioning grows roughly as 10⁵ per doubling of layer count. Sixteen layers is beyond any double precision. The non-uniqueness is not a philosophical point; it is a numerical wall reached at modest model size.

What would finish it: the layer count at which real gravity data (with noise) can resolve anything at all.

10. Derived-column detector — T10

Residual thresholdSensitivityFalse positives
0.02 dex0.000.00
0.05 dex0.000.00
0.10 dex1.000.00

New result. The 0.10 dex threshold reproduces perfectly at a different random seed and sample size — sensitivity 1.00, false positives 0.00. The repaired detector is stable.

What would finish it: running it on five real catalogues.

Which of the ten are worth more work

RankItemWhyCost
1T3 closure third markerCounterintuitive penalty; changes dating practicehours, computation
2T6 estimator auditField-level result available from published numbersdays, reanalysis
3T1 crossing boundA theorem if it holds; small and cleandays, mathematics
4T2 practical K/NGives a design rule for any inverse problemhours, computation
5T10 detector on cataloguesTool is stable; needs a corpusdays, public data

Limits

If one sentence survives: the ten most extensible results all reduce to counting — weightings versus unknowns, sensors versus modes, samples versus particle size — and in three of them the obvious fix (more markers) makes the situation worse.

Best 1,000 overview