Co-culture of blue mussel (Mytilus edulis) and sugar kelp (Saccharina latissima) as a strategy to reduce the predation rate of diving ducks on mussel farms in the Cascapedia Bay (QC, Canada)
Bibliographic record
Abstract
Mussel farming is a well-established industry in eastern Canada that has become, over the last 45 years, an economical pillar for coastal communities. However, production is not consistent, and many factors such as duck predation can influence profitability. In order to reduce the predation rate of diving ducks on blue mussel (Mytilis edulis) farms in Cascapedia Bay, spools of sugar kelp (Saccharina latissima) and an artificial kelp line were introduced above the mussel’s fertilized rope, aiming to act as a visual shield. The survival rate, thus indirectly the predation rate, was calculated by comparing both treatments at 2 specific times: before the ducks arrival and following their departure. The seaweed yield harvested in June 2017 was significantly lower than regional yield obtained in the past (more than 100 fold difference), with an average yield of 25.3g ± 20.3g▪m-1. While no difference was observed between treatments preceding the ducks arrival in the amount (p>0.1), the weight of mussels per linear meter (p>0.3) and the length (p>0.2), a significant increase of weight of mussels per linear meter (7,0%) in favor of the artificial kelp treatment was found (p= 0.02003) after the ducks departure. Although this experiment is believed to represent a valid starting point to explore the possibility of introducing co-culture as a way to financially protect mussel farmers, it does not represent, as of yet, a profitable solution to protect the lines from predation as the yield was not found to be sufficient to sustain the producers.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".