Alberta woodland caribou recovery research and monitoring program to support sustainable forest management
Bibliographic record
Abstract
The intent of the study was to test the hypothesis that wolf occurrence is higher in caribou ranges with more industrial disturbance, provide data for scenario modelling that examines how wolf–caribou predator–prey relationships are predicted to change based on future timber harvest and energy projections, and to provide baseline data for the long-term objective of conducting a province-wide adaptive management experiment that tests the response of caribou and wolf populations to different management options. The probability of wolf occurrence was measured in and around a total of five landscape planning areas that encompassed a total of 17 individual caribou ranges. Blocks were randomly selected and surveyed for the presence of wolves or wolf tracks. Aerial surveys for white-tailed deer were also conducted. Analysis suggests that wolf abundance is comparatively high in those ranges that have high levels of human disturbance. Because deer are now the primary prey of wolves in the system, declines in deer abundance could alter the dynamics between wolves and their multiple prey species. Preliminary results suggest that caribou populations might face a threat when wolves switch prey following a decline in deer numbers.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".