Survival and growth of local and transplanted blue mussels (Mytilus trossulus, Lamark)
Bibliographic record
Abstract
Consumer demand for ¢sh and shell¢sh has led to an increase in aquatic species movement for aquaculture purposes. One potential drawback to the successful transplantation of animals for aquaculture is unpredictable performance due to local adaptation effects. This study used a common environment experiment to examine the potential for local adaptation in Mytilus trossulus (Lamark) on the east coast of Vancouver Island, British Columbia (BC). Newly settled mussels were collected from two sites, Chemainus and Quadra Island (approximately 150 km apart), reared in cages at the Quadra collection site, and mortality and shell length were monitored. Mussels were cage-reared for 384 days with an overall survival of 59% (Quadra) and 39% (Chemainus). The Quadra mussels were initially smaller (5.07mm) than the Chemainus mussels (5.90mm), but the two were similar at the end of the experiment (23.5 and 23.6mm). Transplanted mussels had a significantly higher mortality over the course of the experiment, primarily due to a severe episode in the early summer, and had significantly lower relative growth rates at three of 11 measurement dates. Overall, the local mussels performed better than the transplanted mussels.This study demonstrates the potential for local adaptation effects between populations of mussels separated by only a relatively small geographic distance.
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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 | 0.000 |
| 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 | 0.000 |
| 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".