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)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it