Bibliographic record
Abstract
Challenged to meet the food needs of a growing global population in an environment increasingly impacted by climate change, producers have relied on agricultural intensification to bolster crop yields. However, the proliferation of pesticides in modern agriculture presents a significant threat to freshwater resources and ecosystems. A comprehensive understanding of the presence of agrochemical residues in the environment is critical for assessing sustainable agriculture practices and guidelines for the protection of aquatic life.This research project worked to develop methods for the monitoring of current-use pesticides in agricultural streams: the oft-overlooked small waterways bordering cultivated lands that are the initial point of entry for agrochemical contamination into a watershed. Sampling was conducted in and around the floodplain of Lac Saint-Pierre (LSP), an area of great ecological importance that is now one of the most heavily modified watersheds in Eastern Canada.In total, over two hundred samples were collected from thirty-two sites in five field sampling campaigns over two years, including samples of stream waters, streambed sediments, and periphyton biofilms, where available. A novel method of chemical analysis was developed using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) for the quantitative detection of 31 physiochemically-diverse current-use pesticides in aqueous samples. The application of online solid phase extraction (SPE) with a hydrophilic-lipophilic balance (HLB) column allowed for minimal sample manipulation and analysis of stream water samples within hours of collection in the field. A complimentary method was developed employing pressurized liquid extraction (PLE) to extract pesticide residues from streambed sediments. Pesticide data obtained from the field sampling campaigns was compiled into a robust dataset and correlations were identified between agricultural land use within the catchment areas and the presence of pesticides.This research project involved an ambitious effort to develop novel analytical methods for simplified multiresidue pesticide analysis and demonstrate their utility with application to headwater streams in the field. As global pesticide use continues to grow, it is hoped that the knowledge contributions of this research project will support future efforts to monitor pesticide contamination and protect our water resources
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".