An ecosystem modelling approach for deriving water quality criteria
Bibliographic record
Abstract
Ecological effects of chemicals on ecosystems are the result of direct effects of the chemical, determined in single-species toxicity testing, and indirect effects due to ecological interactions between species. Current experimental methods to account for such interactions are expensive. Hence, mathematical models of ecosystems have been proposed as an alternative. The use of these models often requires extensive calibration, which hampers their use as a general tool in ecological effect assessments. Here we present a novel ecosystem modelling approach which assesses effects of chemicals on ecosystems by integrating single-species toxicity test results and ecological interactions, without the need for calibration on case-specific data. The methodology is validated by comparing predicted ecological effects of copper in a freshwater planktonic ecosystem with an experimental ecosystem data set. Two main effects reflected by this data set (a decrease of cladocerans and an increase of small phytoplankton) which were unpredictable from single-species toxicity test results alone, were predicted accurately by the developed model. Effects on populations which don't interact directly with other populations, were predicted equally well by single-species toxicity test results as by the ecosystem model. The small amount of required data and the high predictive capacity can make this ecosystem modelling approach an efficient tool in water quality criteria derivation for chemicals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".