Incorporating temporal heterogeneity in environmental conditions into a somatic growth model
Bibliographic record
Abstract
Evaluating environmental effects on fish growth can be challenging because environmental conditions may vary at relatively fine temporal scales compared with sampling occasions. Here we develop a Bayesian state-space growth model to evaluate effects of monthly environmental data on growth of fish that are observed less frequently (e.g., from mark–recapture data where time between captures can range from months to years). We assess effects of temperature, turbidity, food availability, flow variability, and trout abundance on subadult humpback chub (Gila cypha) growth in two rivers, the Colorado River (CR) and the Little Colorado River (LCR), and we use out-of-sample prediction to rank competing models. Environmental covariates explained a high proportion of the variation in growth in both rivers; however, the best growth models were river-specific and included either positive temperature and turbidity duration effects (CR) or positive temperature and food availability effects (LCR). Our approach to analyzing environmental controls on growth should be applicable in other systems where environmental data vary over relatively short time scales compared with animal observations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".