Forensic Scatology: Preliminary Experimental Study of the Preparation and Potential for Identification of Captive Carnivore Scat
Bibliographic record
Abstract
Carnivore scats recovered from animal attack and/or scavenging contexts frequently contain forensic evidence such as human bone fragments. Forensic cases with carnivore involvement are increasingly prevalent, necessitating a methodology for the recovery and analysis of scat evidence. This study proposes a method for the safe preparation of carnivore scat, recovery of bone inclusions, and quantification and comparison of scat variables. Fourteen scats (lion, jaguar, lynx, wolf, and coyote) were prepared with sodium-acetate-formalin fixative; analytical variables included carnivore individual, species, body size, and taxonomic family. Scat variables, particularly bone fragment inclusions, were found to vary among carnivore individuals, families, species, and sizes. The methods in this study facilitate safe scat processing, the complete recovery of digested evidence, and the preliminary identification of involved animals. This research demonstrates that scat collected from forensic contexts can yield valuable information concerning both the victim and the carnivore involved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".