Usefulness of Bioindicators and Biomarkers in Pollution Biomonitoring
Bibliographic record
Abstract
We have different possibilities and tools to assess the impact of pollution on marine ecosystems. The ecotoxicological approaches are based on the use of biomonitors and biomarkers. They aim to study the effect of toxic chemicals on the biological organisms especially at the population, community and ecosystem levels. The ultimate goal of ecotoxicology is to be able to predict the effects of pollution so that the most efficient and effective action to prevent or remediate any detrimental effect. In order to assess the impact of anthropogenic activities on the aquatic ecosystem and to insure compliance with regulation or guidelines, we use biomonitoring. This kind of approach is based on the use of biological responses in order to assess anthropogenic changes in the environment. Biomonitoring involves the use of indicator species such as filter feeding mollusk bivalves. These organisms tend to accumulate pollutants in their tissues without showing any apparent detrimental effect. Moreover, they could reflect the real bio available fraction of the pollutant. In order to have an early warning system predicting the pollution effects even at low levels, biomarkers were extensively studied. Some of them were validated in both field and in vivo conditions. In the present paper, the usefulness of bioindicators and biomarkers in pollution monitoring are discussed. An overview of results from case studies dealing with in situ, in vivo and transplantation experiments is presented.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".