Have geographical influences and changing abundance led to sub-population structure in the Ahiak caribou herd, Nunavut, Canada?
Bibliographic record
Abstract
We examined the premise that changing abundance and environmental conditions influence the seasonal dispersion and distribution of migratory tundra caribou (Rangifer tarandus groenlandicus). The Ahiak herd’s (north-central Nunavut Territory, Canada) calving shifted from dispersed on islands to gregarious calving on the mainland coast. As abundance further increased, the calving ground elongated east and west such that we proposed a longitudinal climate gradient. As well, the calving ground’s east and west ends are different distances from the tree-line, which dips south closer to Hudson Bay. We proposed that whether caribou winter on the tundra or within boreal forest and the different climate across the long calving ground could contribute to differential survival and productivity such that sub-population structure would result. At the scale of the individual cows (identified through satellite-collars), we did not find inter-annual spatial fidelity to either the western or eastern parts of the calving ground. At the population scale (aerial surveys of calving distribution), we also did not find discontinuities in calving distribution. The spatial association of individual cows during calving compared with their association during the rut was inconsistent among years, but overall, cows that calve together, rut together. At this time and with the available evidence, we could not infer sub-population structure from shifts in dispersion and distribution as influenced by geography and changes in abundance for the Ahiak herd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".