Relationship between stream size, watershed land use, and pesticide concentrations in headwater streams
Bibliographic record
Abstract
A quantitative multiresidue study of current-use pesticides in multiple matrices was undertaken with field sampling at 32 headwater streams near Lac Saint-Pierre in Québec, Canada. A total of 232 samples were collected in five campaigns of stream waters and streambed sediments from streams varying in size and watershed land use. Novel multiresidue analytical methods from previous work were successfully applied for the extraction of pesticide residues from sediments via pressurized liquid extraction (PLE) and quantitative analysis using ultra high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) with online sample preparation on a hydrophilic-lipophilic balance (HLB) column. Of the 31 target compounds, including 29 pesticides and two degradation products of atrazine, 29 compounds were detected at least once. Consistent with other studies, atrazine and metolachlor were the most widely-detected herbicides. Detections were generally higher in water than sediment samples and the influence of land use on pesticide concentrations was only detectable in water samples. Small streams with a high proportion of agricultural land use in their watershed were generally found to have the highest pesticide concentrations. Corn and soybean monoculture crops, specifically, were found to cause the greatest impact on pesticide concentration in headwater streams and correlated strongly with many of the most frequently detected pesticides. This study highlights the importance of performing multiresidue pesticide monitoring programs in headwater streams in order to capture the impacts of agricultural intensification on freshwater ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".