Bioenergetic and limnological foundations for using degree-days derived from air temperatures to describe fish growth
Bibliographic record
Abstract
Degree-days (DD) are an effective metric for quantifying the thermal opportunity for ectotherm growth. There is strong empirical evidence to suggest that DD are useful for describing fish growth and that immature growth increases linearly with DD. However, fish ecology lags behind other disciplines in the widespread adoption of DD. We provide (1) a foundation for the observed linear relationship between immature fish growth and DD and (2) justification for using DD derived from air temperatures as a proxy for DD derived from water temperatures in fish science. We use bioenergetics models and both simulated and empirical water temperatures to show that immature annual and interannual fish growth are approximately linear with water DD. We then use simulated and empirical data to show that air and surface water temperatures are often highly correlated and that immature fish growth is also approximately linear with air DD. By connecting the dots among air temperature, water temperature, and fish growth, we lay the foundation for wider adoption of DD in fish science.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".