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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".