Landscape features and caribou harvesting during three decades in Newfoundland
Bibliographic record
Abstract
Landscapes can influence the distribution of harvesting by influencing animal distribution and hunter access. For species like caribou, Rangifer tarandus, decades-long shifts in abundance and distribution might alter such relationships, but few studies have been conducted at such scales. We examined relationships between landscape features and 21,380 harvest records of migratory caribou in Newfoundland during caribou population growth (1980s), cessation of growth (1990s), and decline (2000s). We focused on features hypothesized to influence the distributions of caribou and hunters: lichen landcover, roads, cutblocks, outfitter camps, power lines, and towns. We uncovered larger harvests by resident hunters of male and female caribou among lichen landcover, likely providing preferred caribou forage, and larger harvests by non-resident hunters of male caribou away from towns, reflecting the locations of outfitter camps. Only during later decades, resident harvests occurred nearer power lines and cutblocks, likely providing hunter access and reflecting risk-prone foraging by caribou. We surmise that the harvest was facilitated by open habitats, preferred by caribou, and anthropogenic features leading to hunter access, especially as the caribou population declined. Such knowledge at broad scales is increasingly important in an era of widespread disruption to landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".