Modelling the impacts of environmental variation on the distribution of blue marlin, Makaira nigricans, in the Pacific Ocean
Bibliographic record
Abstract
Abstract Su, N-J., Sun, C-L., Punt, A. E., Yeh, S-Z., and DiNardo, G. 2011. Modelling the impacts of environmental variation on the distribution of blue marlin, Makaira nigricans, in the Pacific Ocean. – ICES Journal of Marine Science, 68: 1072–1080. Blue marlin are distributed throughout tropical and temperate waters in the Pacific Ocean. The preference of this species for particular habitats may affect its distribution and vulnerability to being caught. The relationships between the spatial pattern of blue marlin abundance and oceanographic conditions, which may be influenced by climate change, were examined using generalized additive models fitted to catch and effort data from longline fisheries. Distributions of blue marlin density, based on combining the probability of presence and abundance given presence, indicate that there is annual variation in the distribution of blue marlin and that the population apparently moved east during the 1997–1998 El Niño. The interannual variability in blue marlin distribution appears to be associated with El Niño events and is related to shifts in sea surface temperature and the deepening of the thermocline. Models of catch and effort that include oceanographic variables could be used, given predictions from climate models, to explore future changes in distribution, which could then be used to provide management advice related to time-area closures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".