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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".