Complementary use of motion-activated cameras and unbaited wire snares for DNA sampling reveals diel and seasonal activity patterns of brown bears (<i>Ursus</i> <i>arctos</i>) foraging on adult sockeye salmon (<i>Oncorhynchus</i> <i>nerka</i>)
Bibliographic record
Abstract
The seasonal and diel movements of predators to take advantage of shifts in prey availability are fundamental elements of their foraging ecology, and also have consequences for the prey populations. In this study, we used complementary noninvasive techniques (motion-activated cameras and hair snares) to investigate seasonal and diel activity of brown bears (Ursus arctos L., 1758) along six proximate streams supporting spawning populations of sockeye salmon (Oncorhynchus nerka (Walbaum in Artedi, 1792)) in southwestern Alaska. Camera records over 3 years showed a rapid increase in bear activity around the time salmon arrived in the streams, with differences among streams corresponding to differences in salmon phenology. Bears were active throughout the day and night, but there were clear crepuscular peaks when camera data were pooled. When wire snares (to collect hair samples) were paired with cameras, the data showed similar seasonal patterns, but each technique detected bears missed by the other. Roughly equal numbers of bears left hair but no camera image, and images but no hair, at paired sites. Taken together, the results indicated a close correspondence between bear activity and salmon timing, differences in diel timing among streams, and the complementarity of data obtained by motion-activated cameras and hair snares.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".