<em>In Situ</em> Filtration Rates of Blue Mussels (<em>Mytilus edulis</em>) Measured by an Open-Top Chamber Method
Bibliographic record
Abstract
Blue mussels, Mytilus edulis , form dense beds of both commercial and ecological importance, and many attempts have been made to determine their filtration rate. The total time in which mussels actually utilise their filtration capacity in nature varies greatly, making in situ methods for filtration rate measurements relevant. Further, it is being debated to what extend filtration rates measured in the laboratory using cultivated algal cells may apply for mussels in nature. In the present study, we have used an open-top chamber setup in order to allow repeated in situ filtration rate measurements of M. edulis using ambient natural phytoplankton and free-living bacteria. We found that the in situ measured filtration rates are comparable to filtration rates obtained in laboratory studies using different methods and controlled diets of cultivated algal cells. Further, we found that the retention efficiency of free-living bacteria was between 22.2% and 29.9%, in good agreement with values from laboratory studies. Our findings support the assumption that mussels in nature tend to use their filtration capacity when the phytoplankton concentration is above a certain lower trigger level.
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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".