Bibliographic record
Abstract
Except under extremely deviant conditions, the “standard curve” regressional relationship between optical absorbance and absorbent concentration is shown to exhibit an S-shaped profile of progressively increasing slope up to an inflexion followed by a region of progressively decreasing slope. Applied to this profile, a Gaussian regression procedure is shown to deliver i) a versatile, accurate representation of these sigmoid deviations from the Beer’s Law proportional relationship between absorbance (A) and absorbent concentration (C), namely Ar = a exp[ – {(C – b) / c}2 + d in which a, b, c and d are experimentally determined constants ii) an explicit inversion of this regressional relationship C = b + c sqrt[ ln{ a / (Ar – d)}] which is required to determine the absorbent concentration corresponding to an observed value of absorbance, and iii) an expression for the slope of the regressional relationship dAr / dC = – 2(a / c2) (C – b) exp[ – (C – b)2 / c2 ] as a measure of the sensitivity of the experimental procedure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".