Estimating natural and fishing mortality and tag reporting rate of southern rock lobster (<i>Jasus edwardsii</i>) from a multiyear tagging model
Bibliographic record
Abstract
Fishing and natural mortality rates and tag reporting rate for rock lobsters (Jasus edwardsii) in northwest Tasmania, Australia, were estimated using multiyear tagging models. These estimates are necessary for assessment of the resource. Several models were examined that had either two or three tagging events each year, and either combined sexes or kept sexes separate. The model that best described the dynamics of the fishery utilized three tagging events within a year. The year was divided into discrete periods and, within each year, fishing effort and duration of period were used to apportion fishing and natural mortalities, respectively, to the periods. The separation of fishing mortalities by sex was not found to improve the models. Although high (1.01.2·year1), the instantaneous fishing mortality estimates were comparable to estimates obtained from other methods and the relative standard errors were low. Reporting rate estimates were also precise and indicated a lack of participation by the fishing industry. Estimates of natural mortality were low (0.000.02·year1) but imprecise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".