Model for growth and survival of mussels Mytilus edulis reared in Prince Edward Island, Canada
Bibliographic record
Abstract
Mathematical models of commercially important species enable one to integrate the diversity of information on these species, understand mechanisms responsible for observed population dynamics, and assess management scenarios. We present a population model for blue mussels Mytilus edulis grown in suspended culture in 2 bays in Prince Edward Island, Canada. The model incorporates a number of ecological processes, namely allometric growth of individual mussels, temperature-dependent growth rates (based on the Lassiter-Kearns equation), and survival of mussels based on self-thinning. Analysis of our model suggests that the optimal temperature for mussel growth is 15.8C. Also, survival does not depend on site or year, indicating that self-thinning is probably due to competition for space rather than food or other site-specific conditions. Based on sensitivity analyses, growth predictions are robust to changes in parameter values, while survival predictions are quite sensitive to changes in the strength of the effect of initial mussel density and of self-thinning. Evaluation of management scenarios over one grow-out period indicates that date of deployment strongly affects time for seeds to reach commercial size. Optimal initial mussel density depends on whether one wants to maximise the proportion of mussels surviving to harvest or the number of mussels available at harvest; this decision depends on whether seed availability or lease area is limiting.
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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".