Testing the use of remote cameras to index body condition in brown and black bears
Bibliographic record
Abstract
Abstract: Body condition of large mammals is a highly labile parameter that is influenced by recent nutrition, the seasonal hormonal cycle, intrinsic traits such as age and reproductive status, and extrinsic stress factors such as predation risk, social dominance, or human disturbance. Body condition thus integrates many population and individual level factors into a single measure of performance, making it useful in long-term population monitoring. We measured body condition of brown (grizzly) and black bears (Ursus arctos and U. americanus) using remote cameras during 2016-2024 in southwest Canada and compared these data with measures derived from live-captured brown bears from the same study area. We found that on average, photograph-derived measures of body condition predicted late-summer and autumn weight gain similar to weight-per-length and body fat metrics measured from live-captured bears. The photo-derived metric predicted more modest declines in condition in spring than body fat whereas the weight-per-length metric did not indicate declines in condition through spring. We tested the repeatability of the photo measurements using untrained observers and the methods were generally repeatable but can be improved with training. Photographs are much less invasive than live capture–based condition metrics and they allow the comparison of body condition among individuals, seasons, and populations through time, while also considering the variation within the sample. Collection of many samples should enable the examination of novel aspects of the dynamics of population trend and individual performance of bears, and perhaps other species. Note: These data and R scripts were used for the analysis the paper "Testing the use of remote cameras to index body condition in brown and black bears" which was published in the journal URSUS early in 2026.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".