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Record W20909193 · doi:10.1520/jfs45305j

Skeletal Manifestations of Bear Scavenging

2000· article· en· W20909193 on OpenAlexaboutno aff
Evan W. Carson, Vincent H. Stefan, J. Powell

Bibliographic record

VenueJournal of Forensic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsUrsusScavengerCarnivoreTaphonomyOsteologyGenusArchaeologyZoologyGeographyEcologyBiologyMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.237
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations66
Published2000
Admission routes1
Has abstractyes

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