The quantitative use of parasite data in multistock modelling of South African sardine (<i>Sardinops sagax</i>)
Bibliographic record
Abstract
Differences in parasite infection have previously been used to distinguish between fish stocks. We demonstrate a novel use of parasite prevalence-by-length data to inform quantitatively on stock mixing. An initial two mixing stock hypothesis proved consistent with biological and survey data, suggesting that there are different stocks of sardine off the west and south coasts of South Africa. That hypothesis assumed that only recruits moved from the west to the south stock. However, new “tetracotyle”-type metacercarian parasite bio-tag data indicate a need to allow older fish to move between the stocks as well. We demonstrate extension of bio-tagging to inform on the plausibility of population structure hypotheses by including parasite prevalence-by-length data in the model’s likelihood. Our method enables the estimation of the magnitude of mixing between semidiscrete stocks, providing more precise estimates of annual movement. Such improved precision may be important in better informing future movement hypotheses and thereby management. Our research provides a framework to use to inform quantitatively on stock structure and movement hypotheses for other fish species with bio-tagging data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".