Using temperature-dependent embryonic growth models to predict time of hatch of American lobster (<i>Homarus americanus</i>) in nature
Bibliographic record
Abstract
Hatch time of American lobster (Homarus americanus) varies between years and regions, which affects temperature experienced by the developing larvae and hence the time and distance these drift before settling. Hatch time can be assessed by working with fishermen and inspecting the brood of gravid females caught in their traps. However, this would require frequent sampling as the hatch period is protracted (≈7–12 weeks) and would require dedicated sampling in many regions where hatching occurs outside of the fishing season. To address these limitations, we tested the accuracy with which hatch time can be predicted by taking egg samples during the fishing season and estimating embryo development using embryonic eye size (Perkins eye index) and lab-derived, temperature-dependent development functions. Using a linear development function and observed variability in Perkins eye index at hatch, we successfully predicted 100% of the observed 50-day hatch period, and 96% of predicted hatch dates fell within this period. Our results suggest that samples can be obtained in collaboration with fishermen to predict the timing and progression of hatch of American lobster.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".