Biomonitoring Climate Change and Pollution in Marine Ecosystems: A Review on<i>Aulacomya ater</i>
Bibliographic record
Abstract
The sedentarism and wide global distribution of the blue musselMytilus edulishave made it a useful bioindicator to assess changes in the health status of the marine ecosystem in response to pollution and other environmental stresses. Effective biomonitoring of an ecosystem requires, however, that multiple biomarkers be used to obtain an accurate measure of the cumulative effects of different sources of environmental stress. Here, we provide a first integrated review of the biological, economical, and geographical characteristics of another species of mussels, the ribbed musselAulacomya ater. We discuss the use ofAulacomya ateras a complementary biomonitor to the blue mussel to assess the impact of pollutants and climate change. Recent findings have indeed shown thatMytilus edulisandAulacomya aterhave distinctive anatomy and physiology and respond differently to environmental stress. Monitoring of mixed beds containing blue and ribbed mussels may thus represent a unique opportunity to study the effect of environmental stress on the biodiversity of marine ecosystems, most notably in the Southern hemisphere, which is particularly sensitive to climate change and where both species often cohabitate in the same intertidal zones.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".