Using Camera Collars to Study Survival of Migratory Caribou Calves
Bibliographic record
Abstract
ABSTRACT Monitoring survival of juveniles in wild populations of vertebrates is challenging because capture and marking of neonates may influence survival and induce biases. Camera collars have proven effective in resource and habitat selection studies, but their effectiveness to assess offspring survival is unknown. Our objective was to monitor the survival of neonates using camera collars installed on 24 preparturient female migratory caribou ( Rangifer tarandus ) of the Rivière‐aux‐Feuilles herd, from 2016 to 2018, in Nunavik, Canada. Females were captured with a net gun fired from a helicopter and pregnancy was confirmed by ultrasound. Cameras recorded a 10‐second video every 20 min from 1 June until 1 September 2017, when the collar detached automatically. We used Cormack‐Jolly‐Seber models to assess survival and resighting probabilities of calves based on their observation in the videos. Three collars failed, recording less than 30% of the expected videos, including one on a female that did not give birth. Among the 21 females wearing a functional collar, one gave birth to a stillborn calf. We analyzed 25,820 videos recorded from 20 collars. Calf sightings in videos were less frequent as the monitoring period advanced, but we estimated the probability of observing a live calf at 0.77 (SE = 0.42) over the sampling period. Videos indicated a survival rate of 0.67 (SE = 0.11) from birth to 1 September. Our results suggest that camera collars installed on adult females can be used to reliably assess offspring survival, and thus improve our understanding of caribou population dynamics. The application of camera collars should be useful for other large vertebrate species for which the assessment of neonate survival is lacking or difficult to obtain. © 2021 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".