Low Cost Monitoring of Glyphosate in Surface Waters Using the ELISA Method: An Evaluation
Bibliographic record
Abstract
Concerns have been raised in the scientific community regarding the environmental implications of a dramatic increase in corn-based ethanol production and associated increases in pesticide use. The use of glyphosate, a broad-spectrum herbicide, for corn production has increased considerably in recent years in Canada and the United States. The cost of measuring concentrations of organic contaminants in the environment using traditional wet chemistry methods can be prohibitive; especiallywhen large numbers of samples are required to quantify the spatial and temporal variability in contaminant concentrations. The goal of our study was to evaluate a cost-effective method to measure glyphosate concentrations in surface waters. The reliability of enzyme-linked immunosorbent assay (ELISA) results was evaluated against liquid chromatography tandem mass spectrometry, and linear regression results for 30 water samples from urban watersheds revealed a strong relationship (R2 = 0.88). These results suggest that ELISA methods, used in conjunction with traditional methods, represent a cost-effective approach to enhance the spatial and temporal resolution of a water quality monitoring study. Additionally, we measured a total of 739 surface water samples from over 150 sampling locations throughout Ontario using ELISA from April to October 2007. Concentrations exceeded the method detection limit of 0.1 microg/L in 33% of the samples, with a maximum concentration of 12.0 microg/L. Glyphosate showed a bimodal temporal distribution with peak concentrations occurring in late spring/early summer and fall, and did not exceed the Canadian Council of Ministers of the Environment (CCME) guideline for the protection of aquatic life (65 microg/L) in any of the samples.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".