Occurrence of regulated pollutants in populated Mediterranean basins: Ecotoxicological risk and effects on biological quality
Bibliographic record
Abstract
Chemical stressors co-occur in mixtures into watercourses and this complicates predicting their effects on their ecological status. Our knowledge of river basin specific pollutants (RBSPs) is still limited, but it remains necessary to ensure the good chemical and ecological status. We performed an exercise on Mediterranean river sites exposed to urban and industrial pressures in order to, i) prioritize the occurring chemicals, ii) assessing the site's specific chemical risk (RQ site ), and iii) relating the chemical risk to the biological quality, using as evidences invertebrates and diatom indices. Mediterranean rivers suffer from strong pressures which lead to a poor dilution ability, which makes the inhabiting biota highly vulnerable. The most frequent pollutants in the 89 sites surveyed included pharmaceutical products such as the antibiotics azithromycin , clarithromycin , and erythromycin , and the anti-inflammatory diclofenac, and products of industrial origin such as perfluorinated PFOS , nickel, and nonylphenol . Both the diatom index IPS and the macroinvertebrate index IBMWP hold strong negative correlations to RQ site , indicating a significant contribution of chemicals to biological impairment. Chemical contaminants (but not nutrients or dissolved organic carbon) were associated with significant changes to the taxonomic composition of invertebrate communities, but not to that of diatom communities. Our analyses indeed reveal that the impact of co-occurring chemicals translates onto negative effects in the biological quality. Our approach may be of use to evidence impacts on water resources and water quality in rivers under strong human pressure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".