Comparison of aquatic system models using outdoor mesocosm data for ecological risk assessment, part I: methodology
Bibliographic record
Abstract
Mesocosm studies are conducted in the context of higher-tier ecological risk assessments (ERAs) to integrate environmental conditions and study species interactions within waterbodies in agricultural landscapes. Aquatic system models (ASMs) could provide tools to extrapolate the dynamics and effects of chemicals observed in mesocosm studies to a wider range of environmental conditions and exposure scenarios. In this article, we present the methodology of a ring study with four ASMs (AQUATOX, CASM, StoLaM+, Streambugs) applied to data from mesocosm studies, while the results of the ring study are presented in a companion article to be published alongside this work. The ring study aimed to test the feasibility and capability of ASMs to represent mesocosm data and to evaluate if such models can be used as an extension of mesocosm experiments in ERAs. The ring study methodology allowed for model comparison and identified models' strengths and limitations in representing the ecosystem dynamics in control and treated mesocosm studies. Groups of species were defined to map the diversity of the mesocosm ecosystem in the taxa represented by the models, and a consensus trophic web was agreed on to ensure harmonization among the models. Control and effect calibration criteria were developed to evaluate and compare model performances for each species group, explicitly considering the variability in mesocosm data. Our proposed methodologies and the challenges in using mesocosm data to inform modeling approaches are discussed. Finally, we derived recommendations for future research to facilitate the development of models representing mesocosms to support ERAs.
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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 | 0.012 | 0.017 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".