Parasite responses to pollution: what we know and where we go in ‘Environmental Parasitology’
Bibliographic record
Abstract
Environmental parasitology deals with the interactions between parasites and pollutants in the environment. Their sensitivity to pollutants and environmental disturbances makes many parasite taxa useful indicators of environmental health and anthropogenic impact. Over the last 20 years, three main research directions have been shown to be highly promising and relevant, namely parasites as accumulation indicators for selected pollutants, parasites as effect indicators, and the role of parasites interacting with established bioindicators. The current paper focuses on the potential use of parasites as indicators of environmental pollution and the interactions with their hosts. By reviewing some of the most recent findings in the field of environmental parasitology, we summarize the current state of the art and try to identify promising ideas for future research directions. In detail, we address the suitability of parasites as accumulation indicators and their possible application to demonstrate biological availability of pollutants; the role of parasites as pollutant sinks; the interaction between parasites and biomarkers focusing on combined effects of parasitism and pollution on the health of their hosts; and the use of parasites as indicators of contaminants and ecosystem health. Therefore, this review highlights the application of parasites as indicators at different biological scales, from the organismal to the ecosystem.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".