Influence of data quality and quantity from a multiyear tagging program on an integrated fish stock assessment
Bibliographic record
Abstract
Using a modeling framework for toothfish (Dissostichus spp.) population dynamics, fishing, and data collection, this study investigated how the bias and precision of biomass estimates from an integrated tag-based assessment are influenced by various aspects of a multiyear tagging program, particularly the effects of the size of tagged fish compared with the size of fish in the catch (tag size-overlap), numbers of tagged fish, duration of the tagging program, using catch-at-length or catch-at-age data as auxiliary data, and stock depletion levels. Biomass estimates generally improved with more and better-quality tagging data. The results showed that even when tag releases were distributed over a relatively large number of size classes, low recapture numbers collected in short tagging programs with a 100% tag size-overlap were sufficient for robust and unbiased assessments. Particularly in the early stages of the tagging program, a high tag size-overlap was imperative to maximize the likelihood of a robust assessment. Biomass estimates were largely unaffected by the stock depletion level; however, using catch-at-age compared with catch-at-length improved recruitment estimates and resulted in more conservative biomass estimates.
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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 | 0.136 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".