Pig‐mentation: Postmortem Iris Color Change in the Eyes of <i>Sus scrofa</i>*
Bibliographic record
Abstract
Experienced forensic pathologists and examiners may be familiar with the phenomenon of postmortem iris color change; however, only Knight, Simpson's forensic medicine, Arnold, London, 1997; Ref. 1 and Saukko and Knight, Knight's forensic pathology, 3rd ed., Arnold, London, 2004; Ref. 2 have referred to it in the literature, and to date, there have been no published scientific research studies on this taphonomic artifact. A controlled experiment was conducted of postmortem changes to isolated Sus scrofa eyes. The eyes (n = 137) were separated into three groups and each sample was observed for 3-day postmortem at a different temperature. In addition, a Sus scrofa head was obtained to observe postmortem changes of eyes in situ. All isolated blue eyes in the experiment, at room temperature and higher, changed to brown/black within 48 h. The in situ blue eye, at room temperature, turned brown/black within 72 h. If iris color consistently changes postmortem in humans, then this taphonomic artifact must be incorporated into victim identification protocol, including disaster victim identification software, and autopsy reports to prevent inaccurate victim identification and inappropriate exclusion from the identification process.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".