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Record W2068608962 · doi:10.1021/jf020761g

Comparison of a Direct ELISA and an HPLC Method for Glyphosate Determinations in Water

2002· article· en· W2068608962 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDetection limitHigh-performance liquid chromatographyChromatographyTap waterGlyphosateChemistryLinear rangeQuantitative analysis (chemistry)Environmental scienceBiologyEnvironmental engineeringBiotechnology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.273
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it