Bayesian analysis of dolly varden mark-recapture data in canadian arctic
Bibliographic record
Abstract
The estimation of survival parameters is of particular interest within ecological systems for obtaining underlying biological information and animal mark-recapture (capture-recapture) data are often collected for survival parameter estimation studies (King, 2012). However, survival parameters and some other parameters that researchers might be interested (for example, recapture probabilities) are often subject to individual heterogeneity and affected by environmental effects and observation errors. Besides that, recent interest has included additional complexities such as individual covariance and random effects within the statistical framework. To meet the requirements of summarizing unbiased biological information from complex mark-recapture or mark-recapture-recovery data, novel Bayesian fitting state-space models provide a practical tool by coupling a model of mechanistic movement properties (known as process model) with a model of the observation methods (known as observation model). In this thesis, the Cormack–Jolly–Seber (CJS) model and the multi-state models are developed within Bayesian state-space framework were applied to analyze mark-recapture Dolly Varden data collected from five river systems in Canadian Arctic area. In model fitting process, the Markov Chain Monte Carlo (MCMC) method is being applied to in the estimation process to draw the samples to mimic the joint posteriror distribution since the necessary integration for posterior distribution is intractable. The Bayesian latent variable approach is also used to improve the performance of MCMC algorithm. Meaningful biological information is summarized from the Dooly Varden data which can be helpful to build a environment friendly and sustainable fishing community.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".