Diagnostics for multiyear tagging models with application to Atlantic striped bass (<i>Morone saxatilis</i>)
Bibliographic record
Abstract
Information on age- and year-specific survival can be obtained from multiyear tagging data using one of three classes of tag recovery models. Two of the model types yield information on total survival, while the third allows separation of total mortality into its fishing and "natural" components if information on the tag reporting rate is available. The performance of each class is usually assessed using goodness-of-fit tests, Akaike's information criterion, and similar measures. However, we propose that examination of model residuals is also important for the evaluation of model performance and contend that at least four types of problems are potentially detectable via patterns in residuals. Those presented in this paper include nonmixing of newly tagged animals, emigration of older animals, cohort effects associated with initial tag-induced mortality or tag shedding, and a change in the natural mortality rate. We present the diagnostic procedures by analyzing a hypothetical tagging data set and discuss the various constraints inherent to the residuals of each class of models. The diagnostic procedures are also used to evaluate striped bass tagging (Morone saxatilis) data from the Hudson River and Chesapeake Bay.
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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 | 0.031 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".