Comparison of a Direct ELISA and an HPLC Method for Glyphosate Determinations in Water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A competitive direct enzyme-linked immunosorbent assay (ELISA) and high-pressure liquid chromatographic (HPLC) methods were compared in terms of accuracy and precision for the detection and quantification of glyphosate-spiked Nanopure, tap, and river waters. The ELISA had a detection limit of 0.6 ng mL(-)(1) and a linear working range of 1-25 ng mL(-)(1), whereas the HPLC method had a detection limit of 50 ng mL(-)(1) and a linear working range of 100-10000 ng mL(-)(l). No statistically significant differences (95% confidence interval) were found between the ELISA and HPLC analysis of the three water matrixes. The coefficients of variation obtained with the ELISA in tap water were between 10 and 19%, whereas the coefficients of variation for the HPLC analysis were between 7 and 15%. The use of ELISA for the analysis of glyphosate in water is a cost-effective and reliable method capable of meeting water quality guidelines established for Europe and North America.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it