Comparison of Woodland Caribou Calving Areas Determined by Movement Patterns Across Northern Ontario
Bibliographic record
Abstract
ABSTRACT Adult female survival and calf recruitment influence population dynamics, but there is limited information on calving and neonatal mortality of boreal woodland caribou (Rangifer tarandus caribou; caribou) in Ontario, Canada. We identified calf parturition sites and 5‐week neonatal mortality using a movement‐based approach across 3 northern Ontario study regions (Pickle Lake, Nakina, and Cochrane) that vary in their capacity to support caribou populations. In comparing 22 caribou‐years of video‐collar footage during 2010–2013 to predictions of the movement‐based approach, we found live parturition events were 100% correctly classified, date of parturition was within 1.08 ± 0.28 ( ± SE) days, and mortality events up to 5 weeks postpartum were 88% correctly classified. Across study regions, 87% of 186 caribou were pregnant and 76% of 107 caribou‐years indicated birth events with median parturition dates a week later in Cochrane (23 May) than in Pickle Lake (17 May) and Nakina (16 May). Based on selection ratios of caribou‐years with calves‐at‐heel (n = 80), caribou consistently selected for lowlands and closed‐canopied forests and mostly against early‐seral stands (<20 yrs old) and areas near linear features during the neonatal and the post‐neonatal period (up to 35 days postpartum). Based on the video footage and movement models, 30% of 81 caribou‐years that indicated live births also showed females lost their calf within the first 5 weeks postpartum, with higher risk of neonatal mortality associated with increased use of lowlands and greater postpartum movement rates. This study provides informative metrics of caribou reproduction across northern Ontario that will contribute to future population modeling and identifies important landscape features to be considered in future industrial development and land use planning for caribou conservation. © 2020 The Wildlife Society.
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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 | 0.001 |
| 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) | 0.001 | 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".