Identifying optimal variables for machine-learning-based fish distribution modeling
Bibliographic record
Abstract
Machine learning occupies a central position in the modeling of fish distribution patterns. The augmentation of explanatory variables in fish habitat through many kinds of observational methodologies necessitates the discernment of an optimal combination of these variables for fish distribution modeling. We proposed a feature selection technique, recursive feature elimination with cross-validation (RFECV), to determine optimal variables combinations for yellowfin tuna distribution in the Pacific Ocean. Four tree-based models, random forest, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and categorical boosting driven by RFECV, were developed using comprehensive fisheries and biotic/abiotic data. Habitat variables including sea temperature, dissolved oxygen concentration, chlorophyll-a concentration, sea salinity, and sea surface height were identified as significant features by all models. The models were trained using the corresponding selected variables, and these trained models were employed to predict the spatiotemporal distribution of yellowfin tuna from 1995 to 2019. The results obtained could inform useful knowledge for the sustainable exploitation of yellowfin tuna in the Pacific Ocean and furnish a benchmark of feature selection for machine-learning-based distribution modeling of other pelagic species.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".