Assessing the potential responses of 10 important fisheries species to a changing climate with machine learning and observational data across the province of Québec
Bibliographic record
Abstract
Models are needed to predict changes in game fish abundances with respect to climatic factors undergoing change, but such models are often limited by data availability and the capacity of statistical methods to fit challenging ecological datasets. We use current methods in machine learning to describe the responses of 10 fish species to climatic factors across Québec. We assembled a new province-wide, synthetic dataset of fish catches spanning almost 50 years and 6000 sites. Extreme Gradient Boosting (XGBoost) models revealed that climatic factors are more important predictors of trends in game fish catches than nuisance factors (sampling gear, time), lending support to collating other heterogeneous datasets for analyses. Mean annual temperature and precipitation were the most important drivers of species catches. Fish thermal preference guilds predicted primarily species responses to temperature, suggesting that warmer and wetter climates may not favour the same species. Despite the challenging nature of these datasets, XGBoost models provided excellent fit, predictive capacity, and interpretability, thereby illustrating that large, heterogeneous datasets can be used to inform freshwater fisheries management in a changing climate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".