Benford's Law for Quality Assurance of Manner of Death Counts in Small and Large Databases
Bibliographic record
Abstract
To assess if Benford's law, a mathematical law used for quality assurance in accounting, can be applied as a quality assurance measure for the manner of death determination. We examined a regional forensic pathology service's monthly manner of death counts (N = 2352) from 2011 to 2013, and provincial monthly and weekly death counts from 2009 to 2013 (N = 81,831). We tested whether each dataset's leading digit followed Benford's law via the chi-square test. For each database, we assessed whether number 1 was the most common leading digit. The manner of death counts first digit followed Benford's law in all the three datasets. Two of the three datasets had 1 as the most frequent leading digit. The manner of death data in this study showed qualities consistent with Benford's law. The law has potential as a quality assurance metric in the manner of death determination for both small and large databases.
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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 | 0.200 | 0.613 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".