Effects of light intensity and population density on duckweed systems exposed to pesticides: Toxicity and phytoremediation
Bibliographic record
Abstract
S -metolachlor and atrazine are among the most used and detected pesticides in surface freshwaters worldwide. Our objective was to test the effects of light intensity (100, 200, and 400 μmol photons m −2 s −1 ), plant density (25 and 60 % coverage), herbicide concentration (20 and 200 μg L -1 for S -metolachlor or 15 and 150 μg L -1 for atrazine), and their interactions on the growth, physiology, and phytoremediation capacity of S. polyrhiza (duckweed). We assessed these factors and their interactions on growth, pigments, photosynthesis, antioxidant system, and the phytoremediation capacity of S. polyrhiza cultivated in greenhouse microcosms for 12 days. One or two tested factors interacted with herbicide concentration, generally resulting in decreased photosynthetic capacity and a consequent reduction in the relative growth rate in plants exposed to higher atrazine concentrations and to both concentrations of S -metolachlor. This indicates a greater survival of S. polyrhiza in response to atrazine than to S -metolachlor, despite S -metolachlor removal reaching 76 % and atrazine 59 % at high density and in the environmentally relevant concentration. Increases in pigments and non-photochemical quenching under pesticide exposure reflected physiological adjustments that helped duckweeds cope with the stress. We showed that increasing light intensity from 100 to 400 μmol photons m −2 s −1 does not improve phytoremediation. Starting with a higher density enhanced phytoremediation in total percentage but reduced efficiency in μg of pesticide per gram of plant fresh weight in the case of S -metolachlor. Our findings provide valuable insights for utilizing duckweeds in controlled or semi-controlled systems for pesticide removal.
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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 | 0.000 | 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".