Impact of the Toxic Dinoflagellate Alexandrium catenella on the Valve Movement of Mytilus edulis: A Comparison between Two Populations with Contrasting Histories Exposure
Bibliographic record
Abstract
Shellfish aquaculture farms, due to their coastal position, face the threat of exposure to harmful algal blooms. Such blooms can release, among others, paralytic shellfish toxins (PST) produced by the dinoflagellate Alexandrium catenella and are known to cause the restriction of bivalve harvesting sites. Shellfish can accumulate PSTs in levels that are poisonous for humans, therefore making them unfit for consumption. Thus, the ability to detect PSTs before they reach the critical threshold is crucial for minimizing losses in the industry. Previous studies have demonstrated that toxic algae detection is possible with the use of an early warning system based on the valve-gaping behaviour of blue mussel Mytilus edulis. However, some studies observed the presence of toxin resistance in other species of bivalves when they are regularly exposed to PSTs. If no resistance is observed whatever the past history of the populations would be with regard to PST exposure, this species could be appropriate as a sentinel candidate. In this study, we compare the valve-gaping behaviour of two blue mussel populations with contrasting long-term histories of PSTs events (i.e., regularly vs. not previously exposed to the PSTs producer) were compared using experimental exposure of A. catenella to M. edulis. It was found that mussels from both populations exhibited similar gaping behaviour patterns when exposed to A. catenella. For both populations, the number of valve closures and closure duration tended to increase in the presence of A. catenella, which suggested an avoidance response to the toxic dinoflagellate. In conclusion, our results support the use of M. edulis without origin discrimination
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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".