Descriptive and experimental studies on the biotic and abiotic determinants of selected pesticide concentrations in prairie wetland water columns
Bibliographic record
Abstract
The determinants of high use agricultural pesticide concentrations in the water columns of Prairie Pothole Region (PPR) wetlands were examined to evaluate if these ecosystems had characteristics of pesticide sinks. For an ecosystem to function as a pesticide sink it needs to receive, retain, and reduce pesticides. A survey of sixty PPR wetlands (distance between two farthest sites 1,700 km) was conducted to determine the extent to which they received high use pesticides (atrazine and lindane). Sixty-two percent of the wetlands were contaminated with either atrazine or lindane. Pesticide presence was directly related to wetland proximity to pesticide use and precipitation prior to sampling. In June-July lindane presence was positively correlated with phytoplankton concentration; however, in August lindane presence was negatively correlated with phytoplankton concentration. Laboratory and in situ (Delta Marsh, MB) experiments showed that phytoplankton can determine pesticide water column concentrations. For instance, phytoplankton can sorb lindane and remove it from the water column through sedimentation. The extent of pesticide sorption to phytoplankton (Selenastrum capricornutum) was directly related to the pesticides' octanol-water partition coefficient. Sorption to phytoplankton decreased volatilization of the pesticide trifluralin. The presence of wetland water column conditions (such as phytoplankton and other particulate matter) increased degradation of atrazine,lindane, and, glyphosate. In situ experiments did not detect any atrazine or lindane photolysis. The limited amount of ultraviolet penetration, due to attenuation by aquatic macrophytes, suspended particulates, and dissolved organic carbon, prevented photolysis from being a significant pesticide reduction mechanism in the studied 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".