Post-mortem interval estimation in the tropical climate of Southern Nigeria
Bibliographic record
Abstract
Post-mortem interval (PMI) estimation is the first step in the investigation of decomposing remains. The absence of locally derived methods, and a dearth of forensic experts, make PMI estimation difficult in Nigeria. This study aimed to assess decomposition rates in southern Nigeria and to derive formulae for PMI estimation by using quantitative variables, accumulated degree days (ADD) and total body score (TBS), using a pig model (n = 20). A longitudinal examination of TBS and ADD was conducted over 14 months, during the dry and wet seasons. Scatter plots between TBS and PMI, and TBS and ADD were used to show decomposition patterns and loglinear random-effects maximum likelihood regression was used to produce linear regression formulae for PMI estimation. Overall, decomposition progressed rapidly. Shorter PMIs were associated with more advanced decomposition when compared to studies in temperate regions. Despite similar average daily temperatures in the wet and dry seasons, there were marked differences in decomposition patterns between the seasons, with the wet season exhibiting more rapid decomposition rates. This demonstrates the importance of rainfall and humidity in decomposition rates. The derived regression formulae for ADD and PMI will provide a much-needed location-specific method for PMI estimation in Nigeria and regions with similar climates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".