A review of experimental design in forensic taphonomy: moving towards forensic realism
Bibliographic record
Abstract
criteria for scientific data to be admissible in court. In response, there has been a shift towards quantification of methodology and estimating the postmortem interval. Despite these advances, there are still biases and limitations within the discipline not explicitly addressed in the early stages of experimental design nor in final published works. In this article, unresolved debates with respect to the conductance and reporting of forensic taphonomic research are reviewed, beginning with the nature of experimental cadavers, human or animal analogues and their body size, and second, the forensic realism of experimental setups, specifically with respect to caging, clothing and number of carcases. Pigs, albeit imperfect, are a good model to gain a general idea of the trends that may be seen in humans in subsequent validation studies in facilities where human donors are available. To date, there is no consensus among taphonomists on the extent of the effect that body mass has on decomposition progression. More research is required with both human cadavers and non-human analogues that builds on our current knowledge of forensic taphonomy to answer these nagging questions. This will enable the discipline to make the reliable assumption that pigs and donor decomposition data can be applied to homicide cases. A suite of experimental design aspects is suggested to ensure systematic and standardized data collection across different biogeoclimatic circumstances to identify and quantify the effects of potential confounding variables. Such studies in multiple, varied biogeographic circumstances with standardized protocols, equipment and carrion will facilitate independent global validation of patterns. These factors are reviewed to show the need for adjustments in experimental design to ensure relevance and applicability of data within locally realistic forensic situations. The initiation of a global decomposition data network for forensic taphonomists is recommended.Key pointsPigs are a valuable, albeit imperfect, proxy for human decomposition studies.There are few or conflicting data on effects of carcase size, carrion ecology, exclusion cages and scavengers.We recommend single, clothed, uncaged carcases for baseline research to reflect regionally specific forensic casework.
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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 | 0.134 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".