Methyl parathion toxicity in vegetated and nonvegetated wetland mesocosms
Bibliographic record
Abstract
Methyl parathion (MeP) was introduced into constructed wetlands for the purpose of assessing the influence of emergent vegetation on transport and toxicity of the pesticide. Two vegetated (90% cover, mainly Juncus effusus) and two nonvegetated wetland cells (each with a water body of 50 x 5.5 x 0.2 m) were each dosed with 6.5 m3 of water containing active ingredient of MeP at 6.6 mg/L associated with suspended soil at 400 mg/L to simulate a storm runoff event. Acute toxicity was assessed by sampling benthic macroinvertebrates at 5, 10, 20, and 40 m from the inlet before and 96 h after contamination and by in situ exposure of Chironomus tentans (Diptera) up to 24 h after contamination. Methyl parathion was detected throughout the nonvegetated wetland cells (70 microg/L at 20 m, 8 microg/L at 40 m), whereas the pesticide was not transported through the vegetated wetland cells (20 microg/L at 20 m, < 0.1 microg/L at 40 m). A three-way analysis of variance using contamination (repeated measure variable), location, and vegetation indicated significant negative effects of contamination on various insect taxa, such as mayfly nymphs and caddisfly larvae. Seven out of the total of 15 species revealed a significant contamination x vegetation effect, with individuals in the vegetated wetlands being less affected. Four species showed a significant contamination x location effect, confirming a higher toxicity in the inlet area of the wetlands. A significant three-way interaction of contamination x vegetation x location was detected in Chironomus sp., which was most strongly affected at the inlet area of the nonvegetated wetland cells. The in situ bioassay employing C. tentans confirmed the positive effect of wetland vegetation on MeP toxicity. These results demonstrate the importance of vegetation for pesticide mitigation in constructed wetlands.
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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) | 0.000 | 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".