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Record W2031655867 · doi:10.1139/f09-167

The effect of climate change on the western rock lobster (Panulirus cygnus) fishery of Western Australia

2009· article· en· W2031655867 on OpenAlexvenueno aff
Nick Caputi, R. Melville‐Smith, Simon de Lestang, Alan Pearce, Ming Feng

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsFisheryOceanographyClimate changeEnvironmental scienceOceanic climateGeologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.265
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations104
Published2009
Admission routes1
Has abstractyes

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