Staying cool or staying safe in a human-dominated landscape: which is more relevant for brown bears?
Bibliographic record
Abstract
Pigeon et al. (2016) Staying cool in a changing landscape: the influence of maximum daily ambient temperature on grizzly bear habitat selection. Oecologia 181:1101. doi: 10.1007/s00442-016-3630-5 analyzed the effect of ambient temperature on the habitat selection of grizzly bears (Ursus arctos) in Alberta, Canada. They concluded that temperature played a significant role in bear habitat selection and that it was unlikely that human activity introduced biases to the habitat selection of bears. However, Pigeon et al. did not consider variables related to human activities in their analyses. They also misinterpreted previous research that has accounted for temperature in the habitat selection of brown bears. There is much literature published on the negative effects of human disturbance on wildlife in general and on bears in particular. Downplaying the role of human disturbance could have important negative consequences if, in fact, human disturbance were a more important factor than thermoregulation. Indeed, dismissing the importance of human influence, in the face of contradictory evidence, could tempt managers to disregard an important factor that is difficult and often unpopular to deal with in their conservation plans.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 teacher head, 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".