Parturition Timing and the Pre- and Post-partum Behaviour of Female Moose Assessed Using Animal-borne Video and Movement-Based Approaches
Bibliographic record
Abstract
Limited information on moose calving behaviour, including parturition timing, cow-calf interactions, and pre- and post-partum movement patterns hinder our ability to define calving phenology and habitat use. GPS-collars were deployed on 89 female moose over five years, including 8 with animal-borne video and environmental data (AVED) collection systems, across northern Quebec regions. We assessed the accuracy and precision of six movement-based methods typically used to infer calving dates of moose and other ungulates, by comparing calving dates estimated from GPS-collar movement patterns to AVED video observations of calves and calving. Comparisons of 8 females during 12 calving seasons identified three of the six movement-based methods to be both accurate and precise and we used these three movement-based methods to estimate parturition dates for the larger sample of female moose equipped with GPS collars. Moose parturition dates occurred from May 12 to June 23, with more than 70% of births occurring between May 18 and May 26. Classification of videos from AVEDs revealed that females spent more time walking, standing, feeding, and ruminating the day before calving compared to the day after, when they spent time laying down and licking their calf. Analysis of movement patterns demonstrated that one day before calving, females were located (net square displacement) 2.5 km from their calving site (median; range <0.1 - 16.5 km), then remained highly localized for 7 days post-calving (median 0.2 km; range <0.1 - 4.9 km). Parturition dates varied between regions, by on average three days, but did not vary between years or according to latitude, longitude, autumn temperature, forestry disturbance, or hunting disturbance. AVED observations of moose calving validate some, but not all, movement-based methods employed to estimate parturition dates and aid in defining the timing, behaviour, and movement patterns related to this critical life history phase in moose populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".