Long‐Term Range Recession and the Persistence of Caribou in the Taiga
Bibliographic record
Abstract
Abstract: Spatial patterns can help in understanding the decline and future prospects of threatened species, but the dynamics of range retraction have not been applied to these fundamental questions. I analyzed long‐term changes in occupancy by taiga‐dwelling caribou ( Rangifer tarandus caribou ) to estimate their rate of disappearance and time to extirpation in Ontario, Canada. Patterns of range recession, 1880–1990, indicated that half of historic woodland caribou range has been lost, a rate of disappearance of 34,800 km 2 per decade, and a northward range recession of 34 km per decade. The mean metapopulation density, the abundance of discrete winter groups, was one group per 1,900 km 2 , suggesting an average loss of 18 caribou wintering areas per decade during this period. There was a strong coincidence between the recent southern limits of caribou occupancy and the northern front of forest harvesting, implying an anthropogenic agent of decline. The estimated time to extirpation of forest‐dwelling caribou in Ontario, inferred from the sustained rate of disappearance, was 91 years ( 95% confidence interval: 57–149 years ). The persistence of woodland caribou may depend on spatial separation from human incursion.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".