Predicting the timing of the pediveliger stage of Mytilus edulis based on ocean temperature
Bibliographic record
Abstract
Temperature affects nearly all biological rates and consequently is fundamental to individual development time and timing of phenological events. One example is the duration of the pelagic larval stage of mussels, which is crucial for determining the magnitude and timing of recruitment, as well as population dispersal patterns. Understanding the impact of temperature on the rate of larval development is key to predicting the timing of settlement and optimizing mussel seed collection. Advising mussel farmers on Prince Edward Island about the ideal timing for collector deployment is one of the goals of the Mussel Monitoring Program (MMP). In this study we examine the relationship between the phenology of larval development based on MMP data, and satellite measurements of sea surface temperature. The analyses indicated that the first day of the year on which 10–20% of the pool of mussel larvae reached 250 µm could be predicted using the thermal integral measure growing degree-days. While this finding confirmed the importance of temperature for mussel larval phenology, the effect of other environmental variables such as phytoplankton quantity and quality cannot be dismissed.
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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".