Potential effect of variation in water temperature on development time of American lobster larvae
Bibliographic record
Abstract
Abstract Studies typically assess the effects of temperature on development time, larval drift, and fisheries recruitment in American lobster at a range of constant temperatures. However, in nature, lobster larvae are exposed to varying temperatures, which might result in different development times than would be predicted from mean temperatures alone. To investigate this hypothesis, we conducted a modelling exercise in which we simulated larval development from hatch through stages I–IV under different combinations of mean and variance in temperature. Two thermal scenarios were modelled, the first based on estimated (i.e. interpolated by a model from empirical data) recent historical mean and variability of sea surface temperatures (SSTs) experienced by developing larvae in specific parts of the species' range, and the second based on a broad range of simulated combinations of mean and variability in temperature, including conditions that may be experienced by larvae in the future. The model calculated development times using daily SSTs and temperature-dependent development equations from previous studies of warm- and cold-water origin larvae. For warm-origin larvae, higher variability in temperature resulted in shorter development times at very cold and very warm mean temperatures, and longer development at intermediate mean temperatures, than lower (or no) variability. For cold-origin larvae, the effect of variable temperature was overall much smaller, and opposite to that for warm-origin larvae at very cold and very warm mean temperatures. These results show that lobster larvae experience meaningful variability of water temperature in nature, and that this variability can markedly impact larval development. Thermal variability therefore should be considered when estimating development and drift of lobster larvae, including under scenarios of climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".