Bayesian modeling and simulation methods for fish movements
Bibliographic record
Abstract
Bayesian methods have been popular in modelling complex ecological data collected using modern animal tracking technologies such as acoustic telemetry for multiple reasons, including their extreme flexibility, ability to incorporate prior knowledge and better precision. Acoustic telemetry systems technology has been increasingly used to study fish movement patterns and habitat use and estimate demographic parameters, including survival probabilities and population size. However, the data generated using omnidirectional acoustic telemetry studies are complex, with multiple sources of variability. In this thesis, I develop methods to effectively analyze data generated with omnidirectional acoustic telemetry systems. The thesis consists of four manuscripts with three different Bayesian models in the first three manuscripts: (1) Bayesian state-space modelling approach to estimate the hidden fish movement paths of walleye in lake Winnipeg, (2) Bayesian multi-state mark-recapture models to estimate survival of Arctic char living in multiple habitats of the Cambridge bay region of Nunavut, and (3) Bayesian hierarchical modelling to understand the biological and environmental drivers behind the survival of Cambridge bay Arctic char. In the fourth manuscript, we develop a novel set of fishery metrics to further understand the vulnerability of walleye to fishing activities in lake Winnipeg. Furthermore, the thesis provides practical tools for many challenges that will arise from the planning stage of the study to the data analysis stage of acoustic telemetry studies. In addition, the finding of each study provides valuable and influential information for fishery managers to make effective fish management and conservation decisions that will affect the future of the aquatic species in those regions.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".