The effect of climate change on the western rock lobster (Panulirus cygnus) fishery of Western Australia
Bibliographic record
Abstract
Environmental factors such as the Leeuwin Current (influenced by the El Niño – Southern Oscillation cycle) and westerly winds in late winter – spring significantly affect puerulus settlement of the western rock lobster ( Panulirus cygnus ) fishery. Climate change is causing an increase in water temperature that is seasonally variable, a weakening of westerly winds in winter, and an increase in the frequency of El Niño events. Rising water temperatures over 35 years may have resulted in a decrease in size at maturity and size of migrating lobsters from shallow to deep water, increases in abundance of undersized and legal-sized lobsters in deep water relative to shallow water, and shifts in catch to deep water. The size of migrating lobsters is related to the water temperature about the time of puerulus settlement (four years previously). Climate change effects on puerulus settlement, catchability, females moulting from setose to non-setose, timing of moults, and peak catch rates are assessed. As climate change models project that the warming trend will continue, these biological trends are likely to continue. The changes may have negative (increasing frequency of El Niño events) or positive (increasing water temperature) implications for the fishery, which need to be taken into account in stock assessments and management.
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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 | 0.000 | 0.000 |
| 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".