Chemical tags in otoliths indicate the importance of local and distant settlement areas to populations of a temperate sparid, <i>Pagrus auratus</i>
Bibliographic record
Abstract
Understanding geographic origins of fish is essential to the management of fisheries and protection of critical juvenile habitats. We used natural chemical tags (Mn, Sr, and Ba), characterized from otoliths of 0+ snapper (Pagrus auratus) (approximately 13 months postsettlement), to determine the origins of 1- and 2-year-old (subadult) fish about to recruit to the Victorian fishery. We sampled subadults from eight areas across 700 km of coastline and within the major Victorian fishery, Port Phillip Bay. Maximum likelihood analyses indicated for both cohorts that most subadults in Port Phillip Bay and a significant proportion from west Victorian coastal waters had settled within Port Phillip Bay. The contribution of the Port Phillip Bay settlement area to coastal populations, however, decreased with distance to the west, varied between cohorts, and was negligible at locations over 200 km to the east of the bay. Comparison of elemental tags between 0+ fish of known settlement origin and the subadults indicated that unknown settlement areas may have contributed recruitment to one of the cohorts. These results have highlighted the importance of local settlement areas to sustaining the major Victorian fishery, but small juveniles can migrate large distances from this settlement area and contribute to coastal populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".