Ecological Impacts of Organic Chemicals on Freshwater Ecosystems
Bibliographic record
Abstract
The ecological impacts of organic pollutants on freshwater ecosystems have attracted immense scientific, regulatory, and public attention over the past fifty years. In part, this reflects the significant role that freshwater ecosystems play as a repository for anthropogenic chemicals relative to other systems. Some of the most severe ecological impacts have been documented in freshwater ecosystems from persistent organic pollutants (POPs) such as polychlorinated biphenyls, polychlorinated dioxins and furans, and polycyclic aromatic hydrocarbons. Such chemicals can reside for long periods in freshwater sediments, which can then constitute a continual source to the environment even when direct inputs have ceased. Exposure of freshwater biota at lower trophic levels to persistent chemicals can result in transfer to, and ecological impacts at, higher trophic levels through bioaccumulation and biomagnification. In contrast to historically significant organic pollutants, the pervasive nature of new pollutant classes (e.g. pharmaceuticals, polybrominated diphenyl ethers, and perfluorinated surfactants) in global freshwater ecosystems is beginning to be recognized but the full spectrum of their ecological impacts is poorly understood. In this chapter we review documented and potential ecological impacts of organic chemicals in freshwater ecosystems. We focus predominantly on effects at the population, community, and ecosystem levels but, to the extent that our understanding of impacts at these higher levels is predominantly extrapolated from information derived at lower levels, we also include information at the organism and sub-organism level. In addressing each chemical class, impacts on microbial, plant, invertebrate, fish, and fish-eating bird populations are considered where data exists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".