Predicting eastern oyster (Crassostrea virginica) settlement using growing degree days along the north shore of Nova Scotia, Canada
Bibliographic record
Abstract
The supply of oyster spat is crucial for the sustainability and development of the oyster aquaculture industry. While hatcheries worldwide are increasing spat production, wild spat collection remains prevalent in Atlantic Canada. Existing monitoring programs aid in wild spat collection but are costly and labour-intensive, relying on fieldwork and expert personnel. To complement monitoring programs, mathematical models with varying complexity have been used to predict the settlement of commercially valuable bivalves. These models consider various environmental parameters such as temperature, winds, tides, and food concentrations. In this study, we explore the prediction of settlement in Eastern oysters (Crassostrea virginica) using a simple Growing Degree Day (GDD) model, which considers only one parameter, temperature. The GDD model estimates Larval Development Time (LDT) based on accumulated heat units (°C·day) above a species-specific minimum temperature threshold for growth. By calibrating the model with literature data and validating it with field observations from four estuaries in Nova Scotia, we aimed to provide a tool for farmers to predict the onset of oyster settlement based on observed seawater temperature. The model effectively predicted the onset of oyster settlement based on observed seawater temperature. The GDD model is a simple and easily implementable tool that can enhance the success of wild spat collection efforts and contribute to the robustness of the oyster aquaculture industry.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".