Quantifying forest disturbance regimes within caribou (Rangifer tarandus) range in British Columbia
Bibliographic record
Abstract
Habitat disturbance is a major driver of the decline of woodland caribou (Rangifer tarandus caribou) in Canada. Different disturbance agents and regimes negatively impact caribou populations to different degrees. It is therefore critical that land managers and scientists studying caribou have a detailed understanding of the disturbance regimes affecting caribou habitat. In this work we use recent advances in satellite-based disturbance detection to quantify polygonal forest disturbance regimes affecting caribou ecotypes and herds in British Columbia (BC) from 1985 to 2019. Additionally, we utilize this data to investigate harvesting rates since the implementation of the Species at Risk Act (SARA) and publication of recovery strategies for caribou in BC. Southern Mountain caribou herds are the most threatened yet experienced the highest rates of disturbance, with 22.75% of forested habitat within their ranges disturbed during the study period. Over the study period, we found that in total, 16.4% of forested area was disturbed across all caribou herd ranges. Our findings indicate that caribou in BC face high, and in many cases increasing, levels of habitat disturbance. Our results provide a detailed understanding of the polygonal disturbance regimes affecting caribou in BC at the herd scale, and highlight the need for effective implementation of policies aimed at preserving caribou habitat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".