Southern bluefin tuna (<i>Thunnus maccoyii</i>) shed tags at a higher rate in tuna farms than in the open ocean — two-stage tag retention models
Bibliographic record
Abstract
Tag shedding rates are estimated for southern bluefin tuna (SBT, Thunnus maccoyii) from double-tagging data arising from two tagging studies run in the 1990s and 2000s. Since the early 1990s, a high proportion of SBT tag recoveries has been sourced from juveniles captured by purse seine vessels in the Great Australian Bight and transferred to tuna farms off Port Lincoln in the state of South Australia. When tags have been shed by wild-caught SBT fattened in tuna farms, it is generally not known if the tags were shed in the open ocean before purse seine capture or after purse seine capture while the fish were on farm. Using a Bayesian approach, we fit separate tag retention curves for time in the ocean and time on farms as Weibull distribution reliability functions. The study suggests SBT shed tags at a much higher rate in on-farm enclosures than in the open ocean. Biofouling on tags in tuna farms may contribute to higher tag shedding rates.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".