Movement-based methods to infer parturition events in migratory ungulates
Bibliographic record
Abstract
Long-distance migrations by ungulate species are a globally imperiled natural phenomenon and conservation of them requires monitoring population vital rates. Satellite telemetry tracking is widely used for understanding the spatial distribution and movement of animals, especially migratory animals in remote environments. Recently, analytical methods have been developed to infer parturition events from movement data in multiple species that calve in isolation, but to date such methods have not been tested on animals that both migrate and spatially aggregate during calving. We applied two movement-based methods developed to infer parturition in nonmigratory woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) to 241 reproductive seasons spanning 6 years of GPS data from migratory barren-ground caribou (Rangifer tarandus granti J.A. Allen, 1902). We compared results from both methods to data from aerial surveys of collared females during the calving period. We found that each movement-based method had ∼80% overall accuracy to identify calving events, with interannual variation ranging from 61% to 100%. When we considered instances when the two analytical methods agreed on parturition outcome, the accuracy increased to 89% with an annual range of 73%–100%. Using these methods, we identified marked interannual differences in peak calving dates and higher parturition rates than previously reported for this caribou herd. The successful application of these analyses to a migratory, gregarious ungulate suggests a broader applicability of the methodology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".