Assessing the ability of the growing degree-day metric to explain variation in size-at-age and duration-to-moult of lobsters and crabs
Bibliographic record
Abstract
Environmental temperature directly controls the rate at which ectotherms grow and develop. The growing degree-day metric (GDD, °C·day) scales time by temperature to create a thermal time scale relevant to ectothermic organisms. Here we assess the ability of GDD to model size-at-age and duration-to-moult in 15 datasets (9 size-at-age, 6 duration-to-moult) comprising 7 species of lobsters and crabs. We applied generalized linear models to assess the ability of GDD vs. “calendar” time to explain growth and development observations within and across trials. Best-fit models included GDD with fewer parameters in 6 of 9 size-at-age and 5 of 6 duration-to-moult datasets and a better fit to the data in 6 of 9 size-at-age datasets. Our results show that the individual growth of lobster and crab species can be modelled using thermal time models. Such models can be used to identify thermal tolerance limits, predict growth under varying temperature conditions and disentangle temperature effects from those of other factors affecting individual growth and development, resulting in improved growth models for field conditions including fisheries management.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".