Sampling Flies or Sampling Flaws? Experimental Design and Inference Strength in Forensic Entomology
Bibliographic record
Abstract
Forensic entomology is an inferential science because postmortem interval estimates are based on the extrapolation of results obtained in field or laboratory settings. Although enormous gains in scientific understanding and methodological practice have been made in forensic entomology over the last few decades, a majority of the field studies we reviewed do not meet the standards for inference, which are 1) adequate replication, 2) independence of experimental units, and 3) experimental conditions that capture a representative range of natural variability. Using a mock case-study approach, we identify design flaws in field and lab experiments and suggest methodological solutions for increasing inference strength that can inform future casework. Suggestions for improving data reporting in future field studies are also proposed.
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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 | 0.832 | 0.941 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".