Assessing potential impacts of black bear predation on neonatal mortality in boreal caribou
Bibliographic record
Abstract
Abstract Boreal woodland caribou ( Rangifer tarandus caribou ) populations are declining because of increasing predation that is ultimately attributed to human‐caused landscape alterations and climate change. Bears ( Ursus spp.) can be a primary cause of neonate caribou mortality, yet bear–caribou dynamics during the calving season are poorly understood, particularly in western Canada. Using a simulation parameterized by empirical data from black bears ( U. americanus ) and caribou, we assessed how bear movement, habitat use, and density interact with caribou calving habitat selection to influence predation of caribou neonates. For each simulation, we placed neonates within caribou ranges according to caribou densities and calving dates. We then monitored their fates for 2 weeks, the period during which calves are particularly vulnerable to bear predation. Simulated neonates could be killed when the movement paths of global positioning system (GPS)‐collared bears came within a specified detection distance. We multiplied simulated kill rates by known bear abundance to estimate the number of neonates killed by the entire bear population. Simulation results indicated that individual bears rarely kill neonates because of low bear–neonate spatial overlap, but neonatal mortality can still be high owing to the bear densities regularly observed in the boreal forest. Caribou selected habitat during calving that reduced bear predation compared to calving randomly across their range. Recent efforts to conserve caribou have included predator reductions, but our results highlight that such action would be a challenge for black bears because it would require removing a high number of bears, many of which would never encounter a caribou calf.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".