Exploring the effect of sampling, protogyny, and larval advection on stock estimates subject to no-take closures in a spatially complex coral reef line fishery on the Great Barrier Reef, Australia
Bibliographic record
Abstract
Simulation is used to evaluate the ability of a two-region, age-structured assessment model to provide accurate and precise estimates of stock status (i.e., the ratio of female spawning biomass to unfished female spawning biomass) for coral trout (Plectropomus leopardus) on the Great Barrier Reef (GBR), Australia. The model used to generate the simulated data used by the assessment model is a spatially complex age- and sex-structured population dynamics model that captures the protogynous nature of coral trout. Stock status is underestimated (negatively biased), with the extent of negative bias related to mis-specification of the breeding strategy of the target fish stock, the impact of the amount of larval connectivity among reefs, the number of reefs closed to fishing, as well as exploitation rates. The estimates of stock status were less negatively biased when fishery-independent index and age- and length-composition data were available from closed areas. The results will inform the development of management strategies for coral trout in the GBR and highlight the importance of basing evaluations of estimation and management performance on operating models that capture ecologically important processes such as metapopulation dynamics and protogynous life history.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".