Increase in growth rates of southern bluefin tuna (<i>Thunnus maccoyii</i>) over four decades: 1960 to 2000
Bibliographic record
Abstract
Estimates of long-term temporal trends and variability in growth are often not available for many commercially exploited fish stocks. An integrated estimation framework that combines growth information from tagging studies, direct age estimates from hard parts, and modal progression estimates from lengthfrequency data is applied to data on southern bluefin tuna (Thunnus maccoyii, SBT) collected over four decades, from 1960 to 2000. By using an integrated approach, a comprehensive set of growth estimates can be obtained for each of these four decades even though substantive deficiencies exist in the coverage of the historical data from any single source. The results confirm previous findings that cohorts from the 1980s grew substantially faster at young ages than cohorts from the 1960s. The results also suggest that the 1970s was a period of transition and that growth of fish up to about age 4 was faster in the 1990s than in the 1980s. The changes in SBT growth over these four decades are consistent with density-dependent responses given the history of exploitation and stock assessment estimates of changes in population size.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".