Using camera traps to study behaviour in wild populations: a case study of the brown bear Ursus arctos
Bibliographic record
Abstract
Research on endangered species often relies on behavioural information to acquire data throughout a range of fields. The demographics of a population can be directly measured, yet the study of social behaviour, plasticity, and interactions is somewhat restricted. Brown bears are a species which, due to their solitary and wide-ranging ecology, are thought to rely heavily on chemical signals as a means of communication. Conducted off the west-coast of British Columbia, Canada, we used camera traps orientated towards bear marking trees to assess behavioural differences between age/sex classes, and by season, to interpret the function of chemical signalling in the species. With camera trapping technology advancing, we are now better equipped to study animal behaviour in less invasive ways in the field. By developing techniques we have been able to study complex interactions and behaviours not possible of bears in captivity. Non-invasive methods used in population assessment (e.g. DNA from hair snares) have begun to make use of scent marking behaviour. However, prior knowledge of the relationship between these sites and the species being studied is required to allow for better estimates to be derived, by accounting for behavioural bias in sampling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".