A conceptual model for migratory tundra caribou to explain and predict why shifts in spatial fidelity of breeding cows to their calving grounds are infrequent
Bibliographic record
Abstract
Calving grounds of migratory tundra caribou (Rangifer tarandus) have two prominent characteristics. Firstly, the cows are gregarious, and secondly, the annual calving grounds spatially overlap in consecutive years (spatial fidelity). The location of consecutive annual calving grounds can gradually shift (either rotationally or un-directional) or more rarely, abruptly (non-overlapping). We propose a mechanism to interpret and predict changes in spatial fidelity. We propose that fidelity is linked to gregariousness with its advantages for individual fitness (positive density-dependence). Our argument is based on a curvilinear relationship between the density of cows on the calving ground (which we use to index gregariousness) and spatial fidelity. Extremely high or low densities are two different mechanisms which can lead to reduced spatial fidelity to annual calving grounds and reflect the caribou’s adaptive use of its calving ranges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".