Use of valvometry as an alert tool to signal the presence of toxic algae Alexandrium catenella by Mytilus edulis
Bibliographic record
Abstract
Valvometry is a non-invasive technique used to continuously monitor gaping behaviour of bivalves at high frequency. In previous laboratory studies, valvometry has revealed a behavioural sensitivity of bivalves to the presence of toxic microalgae in seawater. However, the application of valvometry as an early-warning system for detecting natural occurrences of toxic microalgae and their resulting toxicity in bivalves remains largely unexplored. In this study, valvometry was used to characterise changes in blue mussels (Mytilus edulis) gaping behaviour during gradual exposure to the toxic dinoflagellate, Alexandrium catenella, which produces paralytic shellfish toxins (PST). Laboratory experiments were first performed to identify specific gaping behaviour changes and these responses were subsequently validated in natural seawater conditions in a second experiment. Under both laboratory and natural seawater conditions, mussels exposed to A. catenella tended to remain open (yawning) longer than non-exposed mussels. This change in gaping behaviour was observed at PST concentration as low as 30 μg STXeq 100 g–1 of mussel tissue. We suggest that increased opening is likely related to temporary muscular paralysis induced by toxic algae, as this mechanism has been previously reported in other bivalve species. Furthermore, we observed that biological rhythms of valve behaviour related to tidal and daily rhythms were modified when mussels were intoxicated by PSP (Paralytic Shellfish Poisoning). In conclusion, the effects of toxic algae on mussel gaping behaviour reveals that valvometry could be used as an early-warning tool for the presence of toxic Alexandrium sp. in the environment prior to mussels reaching the regulatory threshold (80 μg STXeq 100 g–1) for harvest interdiction.
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.
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 | 0.000 |
| 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 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".