Subpopulation structure of caribou (<i>Rangifer tarandus</i>L.) in arctic and subarctic Canada
Bibliographic record
Abstract
Effective management and conservation of species, subspecies, or ecotypes require an understanding of how populations are structured in space. We used satellite-tracking locations and hierarchical and fuzzy clustering to quantify subpopulations within the behaviorally different barren-ground caribou (Rangifer tarandus groenlandicus), Dolphin and Union island caribou (R. t. groenlandicus x pearyi), and boreal (R. t. caribou) caribou ecotypes in the Northwest Territories and Nunavut, Canada. Using a novel approach, we verified that the previously recognized Cape Bathurst, Bluenose-West, Bluenose-East, Bathurst, Beverly, Qamanirjuaq, and Lorillard barren-ground subpopulations were robust and that the Queen Maude Gulf and Wager Bay barren-ground subpopulations were organized as individuals. Dolphin and Union island and boreal caribou formed one and two distinct subpopulation, respectively, and were organized as individuals. Robust subpopulations were structured by strong annual spatial affiliation among females; subpopulations organized as individuals were structured by migratory connectivity, barriers to movement, and/or habitat discontinuity. One barren-ground subpopulation used two calving grounds, and one calving ground was used by two barren-ground subpopulations, indicating that these caribou cannot be reliably assigned to subpopulations solely by calving-ground use. They should be classified by annual spatial affiliation among females. Annual-range size and path lengths varied significantly among ecotypes, including mountain woodland caribou (R. t. caribou), and reflected behavioral differences. An east-west cline in annual-range sizes and path lengths among migratory barren-ground subpopulations likely reflected differences in subpopulation size and habitat conditions and further supported the subpopulation structure identified.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".