The growing degree-day and fish size-at-age: the overlooked metric
Bibliographic record
Abstract
Growth rate in ectotherms, including most fish, is a function of temperature. For decades, agriculturalists (270+ years) and entomologists (45+ years) have recognized the thermal integral, known as the growing degree-day (GDD, °C·day), to be a reliable predictor of growth and development. Fish and fisheries researchers have yet to widely acknowledge the power of the GDD in explaining growth and development among fishes. We demonstrate that fish length-at-day (LaD), in most cases prior to maturation, is a strong linear function of the GDD metric that can explain >92% of the variation in LaD among 41 data sets representing nine fish species drawn from marine and freshwater environments, temperate and tropical climes, constant and variable temperature regimes, and laboratory and field studies. The GDD demonstrates explanatory power across large spatial scales, e.g., 93% of the variation in LaD for age-2 to -4 Atlantic cod (Gadus morhua) across their entire range (17 stocks) is explained by one simple GDD function. Moreover, GDD can explain much of the variation in fish egg development time and in aquatic invertebrate (crab) size-at-age. Our analysis extends the well-established and physiologically relevant GDD metric to fish where, relative to conventional time-based methods, it provides greater explanatory power.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".