Evaluation of the effects of diuron and its derivatives on <i>Lemna gibba</i> using a fluorescence toxicity index
Bibliographic record
Abstract
The herbicide diuron (DCMU) [3-(3,4-dichlorophenyl)-1,1-dimethylurea] is largely used in agricultural practices which contribute to water pollution in large areas. Its degradation induced by light or microbial activity is known to be a slow process, and may result in the accumulation of DCMU derivatives in the environment. In this report we used the yield of PSII variable fluorescence of Lemna gibba affected by the DCMU derivatives DCPMU [1-(3,4-dichlorophenyl)-3-methylurea], DCPU [1-(3,4-dichlorophenyl)urea], and DCA [3,4-dichloroaniline] to calculate the fluorescence toxicity index. We found the fluorescence toxicity index to be a useful parameter to evaluate the inhibitory effect on PSII electron transport in L. gibba exposed to DCMU and its derivatives. The variations observed for the inhibitory effect between DCMU and its derivatives seem to be caused by the modification of the dimethylurea group within the DCMU molecule. The fluorescence toxicity index demonstrated a strong quantitative dependency between the inhibitory effect of PSII electron transport and pollutant concentrations. We propose the fluorescence toxicity index to be a useful tool for future bioassays in evaluating the quality of water polluted with herbicides that induce an inhibition to PSII photochemistry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".