Skeletal Manifestations of Bear Scavenging
Bibliographic record
Abstract
In many partially or fully skeletonized forensic cases, postmortem animal damage is simply attributed to rodents or carnivores; little effort is made to determine the general size or assign a genus to the scavenger. As one of the largest wild carnivores to inhabit mountainous and forested areas throughout the continental United States, Alaska, and Canada, black bears (Ursus americanus) must be considered possible suspects when skeletonized remains are located showing marks of carnivore damage. Since 1995, three cases of known bear scavenging have been referred to the Maxwell Museum's Laboratory of Human Osteology by the New Mexico Office of the Medical Investigator for skeletal analysis. These cases comprise a total of seven individuals, and all of the remains were deposited in high altitude forests of New Mexico along the western border with Arizona with a minimum of 4 months exposure before recovery. When analyzed, all cases shared a similar pattern of element survivorship and damage. We suggest that bears can be distinguished from members of the canid family, the other common scavenger of human remains, based on the representation of skeletal elements at the scene. Rates and patterns of damage are not as accurate as element recovery in the discrimination of scavenger genus. Use of this information should allow forensic anthropologists to better understand the postmortem taphonomic processes that shaped the skeletal remains, and hopefully prevent misdiagnoses of perimortem trauma on elements not typically scavenged by canids.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".