Dependency of Queensland and the Great Barrier Reef’s tropical fisheries on reef-associated fish
Bibliographic record
Abstract
Coral reefs have been subject to mass coral bleaching, potentially causing rapid and widespread degradation of ecosystem services that depend on live coral cover, such as fisheries catch. Fisheries species in tropical waters associate with a wide range of habitats, so assessing the dependency of fisheries on coral reefs is important for guiding fishery responses to coral reef degradation. This study aimed to determine how fisheries catches associate with coral reefs in Queensland, Australia. Queensland's largest fisheries did not target fish associated with reefs, but specific sectors, particularly aquarium fisheries and commercial fisheries in the mid to northern region had a high dependence on species that use coral reefs. Regions that had a greater relative area of coral reefs had higher catches of species that depend on live coral, suggesting that coral area could be used to predict the sensitivity of a jurisdiction's fisheries to bleaching. Dynamic analysis of stock trends found that coral trout and red throat emperor, the two largest species by catch for the reef line fishery, were at risk of overfishing if habitat loss caused declines in stock productivity. Management of fisheries that are highly dependent on reefs may need to adapt to declining productivity, but further research to support ongoing reforms in Queensland's fisheries is needed to quantitatively link reef degradation to stock production parameters is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".