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Record W2953941332 · doi:10.1002/wer.1167

Analytical methods to analyze pesticides and herbicides

2019· review· en· W2953941332 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

VenueWater Environment Research · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChromatographyPesticideChemiluminescenceChemistryExtraction (chemistry)Environmental chemistryEnvironmental scienceBiochemical engineeringEngineeringBiology

Abstract

fetched live from OpenAlex

Presented in this paper is an annual review of literatures published in 2018 on topics relating to analytical methods for pesticides and herbicides. According to the different techniques, this review is divided into six sections, including extraction methods; chromatographic or mass spectrometric techniques; electrochemical techniques; spectrophotometric techniques; chemiluminescence and fluorescence methods; and biochemical assays. PRACTITIONER POINTS: Totally 134 relevant research articles are summarized. The review is divided into six parts according to the techniques. Chromatographic and mass spectrometric methods are the most widely used.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.190
GPT teacher head0.437
Teacher spread0.248 · 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