Experimental Study of Black Bear (Ursus americanus) and Grizzly Bear (U. arctos) Tooth Marks and Other Gnawing Damage on Bone
Bibliographic record
Abstract
Tooth mark and other gnawing damage modifications on bone from African carnivores have been extensively examined, but there are less data on North American carnivores, especially on Ursidae (bears). The present study examined gnawing damage by captive black bear (Ursus americanus) and grizzly bear (U. arctos) fed 55 proximal or distal femora from cattle (Bos taurus) in order to distinguish ursid gnaw damage characteristics. Tooth mark modifications examined include pits, punctures, scores, and furrows, while other gnaw damage modifications include crenellated margins, edge polish, scalloping, scooping, and crushed margins. Each tooth mark was processed through the open-source software ImageJ in order to obtain the area, perimeter, length, and width. Tooth pits had an average length of 3.5 mm and average width of 2.2 mm; scores had an average width of 1.5 mm. There was a statistically significant difference between ursid tooth pits and those created by various other scavenging species. Other common taphonomic effects included scalloping on the distal end of the femur, especially on the patellar articular surface; scooping on the proximal end of the femur, especially on the greater trochanter; and furrows, primarily on the distal end of the femur along the patellar articular surface and condyles. Cancellous scooping occurred in 35.2% of the entire sample, while scalloping occurred in 29.6% of the entire sample. These high percentages may be distinctive characteristics of ursid gnaw damage and therefore may help distinguish ursid scavenging from that of other carnivores.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".