A Toxic Unit and Additive Index Approach to Understanding the Interactions of 2 Piscicides, 3-Trifluoromethyl-4-Nitrophenol and Niclosamide, in Rainbow Trout
Bibliographic record
Abstract
The toxic unit and additive index approaches were used to understand how 2 pesticides, 3-trifluoromethyl-4-nitrophenol (TFM) and 2,5-dichloro-4-nitrosalicylanilide (niclosamide; Nic), interact in mixtures. Our first objective was to determine whether the interaction was strictly additive or greater than additive at doses comparable to those used to control invasive sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes, and our second was to compare the utility of the toxic unit and additive index models for determining how TFM and Nic interacted. Typically, TFM is mixed with Nic (1-2%, w/v) to increase its potency and reduce TFM use. However, there is little information on how the 2 chemicals interact. Using a well-studied, resident nontarget fish, the rainbow trout (Oncorhynchus mykiss), we conducted toxicity tests with TFM, Nic, and TFM:Nic (100:1, w/v; TFM/1% Nic) mixtures over 12 h to determine if the interaction was strictly additive, less than additive (antagonistic), or greater than additive (synergistic). The toxic unit and additive index approaches indicated synergistic interactions at environmentally relevant concentrations, suggesting that both are valid approaches for predicting how TFM and Nic interact. The toxic unit approach was simpler to conceptualize and to calculate, and we recommend that it be used when describing how TFM and Nic, and other similar organic compounds, interact with each other in aquatic ecosystems. Environ Toxicol Chem 2021;40:1419-1430. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.001 |
| 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 | 0.001 |
| 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".