Estimating survival probabilities of Cambridge Bay Arctic char using acoustic telemetry data and Bayesian multistate capture–recapture models
Bibliographic record
Abstract
Arctic fishes are threatened by climatic change and other anthropogenic stressors, yet information on how such changes impact survival remains scarce. Acoustic telemetry has become valuable for studying aspects of fish ecology, including survival, which is invaluable in understanding potential responses to changing conditions. In Cambridge Bay, NU, we have been using acoustic telemetry to study movements and habitat use of the culturally and commercially important Arctic char ( Salvelinus alpinus). Here, we combine acoustic telemetry data and Bayesian multistate mark–recapture models to study the survival of Arctic char from 2014 to 2018 in the region in freshwater and marine/estuarine habitats. We found that survival probabilities were high (>0.87) and models considering two environments (freshwater and marine) perform better than those considering three (including estuarine habitats). Furthermore, the survival in fresh water was higher than survival in marine/estuary environments. Overall, the results of this study further our understanding of important demographic parameters (i.e., survival) for Arctic char in the region, which will be useful in refining fishery management plans for the largest commercial fishery for this species in Canada.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".