Bibliographic record
Abstract
Understanding how populations are structured and how they use natural and anthropogenic spaces is essential for effective wildlife management. A total of 510 barren-ground (Rangifer tarandus groenlandicus), 176 boreal (R. t. caribou), 11 mountain woodland (R. t. caribou), and 39 island (R. t. groenlandicus x pearyi) caribou were tracked with satellite collars in 1993-2009 in the Northwest Territories, Nunavut, and northern Alberta. Using satellite location data and hierarchical and fuzzy cluster analyses, I verified that Cape Bathurst, Bluenose-West, Bluenose-East, Bathurst, Beverly, Qamanirjuaq, and Lorillard barren-ground subpopulations were robust; the Queen Maude Gulf and Wager Bay barren-ground subpopulations were distinct. Dolphin and Union island caribou formed one population; boreal caribou formed two distinct subpopulations. Females in robust subpopulations were structured by strong annual spatial affiliation; those in distinct subpopulations were spatially independent and structured by migratory connectivity, movement barriers, and/or habitat discontinuity. An east-west cline in annual-range sizes and path lengths supported the subpopulation structure identified for migratory barren-ground caribou. I analyzed satellite location data to determine parturition dates and activity periods for all caribou ecotypes. For parturition dates I found a north-south cline for boreal caribou, west-east cline for migratory barren-ground caribou, and ecotype and subspecies clines for boreal and barren-ground caribou. Based on annual changes in movement rates I identified eight activity periods for boreal and tundra-wintering, 10 for mountain woodland, and 12 for migratory barren-ground caribou. Based distribution and movements, boreal caribou avoided seismic lines during periods when females and calves were most vulnerable to predators or hunters. They crossed fewer seismic lines and travelled faster when they crossed them than expected. Caribou avoided areas ≤400 m from seismic lines where they could space away from them suggesting that they perceive these as risky areas. I defined secure habitats as areas that were >400 m from anthropogenic linear features. Population growth rates were higher in areas where they had access to secure unburned habitat and where most of that was in patches >500 km2. Critical habitat for boreal caribou is a habitat state that provides “security” from predation risk and facilitates the effectiveness of their anti-predator strategies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".