Simulation testing the robustness of a multiregion, tag-integrated assessment model that exhibits natal homing and estimates natural mortality and reporting rate
Bibliographic record
Abstract
The influence of model complexity on integrated tagging and catch-at-age analysis (ITCAAN) parameter estimation is poorly understood for populations exhibiting natal homing. We investigated ITCAAN performance under varying levels of movement, degree of similarity in population productivities, data quality, spatial complexity in parameterization, and whether natural mortality and (or) reporting rates were fixed at actual values, estimated, or misspecified. Dynamics of four populations with natal homing that intermixed during periods of harvest were simulated based on Lake Erie walleye (Sander vitreus). Our results suggest, when high-quality tagging data are available, ITCAAN models are able to simultaneously estimate movement rates, natural mortality, and tag reporting rates, though accuracy and precision of model estimates will decrease with greater model complexity and fewer tags released. Additionally, ITCAAN models may have difficulty estimating individual population abundances under certain movement rates when population productivities are vastly different. ITCAAN models that estimate natural mortality and reporting rates may perform best with similar sized populations and when data are available to assist the estimation of reporting rates.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".