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Record W2164606861 · doi:10.6000/1927-5129.2013.09.73

Determination of Paraquat (Herbicide) Residue Level in Sandy Clay Loam Soil Using High Performance Liquid Chromatography

2013· article· en· W2164606861 on OpenAlexvenueno aff
Y. C. Wong, N. Norsyamimi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsParaquatLoamResidue (chemistry)PesticideOrganic matterChemistryEnvironmental chemistryPesticide residueSoil waterSoil testLeaching (pedology)Soil organic matterAgronomySoil scienceEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Pesticide is considered as the most widely used form of agricultural chemical. Pesticide residue is the remaining pesticide on or in soil after pesticide is applied on agriculture soil. Soil is a component that is crucial to life. The research purposely concerned on major effects that could be contributed from medium to unlimited application of pesticide. The soil samples were systematically collected from 5 plots and Paraquat residues concentrations were determined in dissimilar depth of soil layers. The herbicide used was Paraquat that contained Paraquat Dichloride. High Performance Liquid Chromatography (HPLC) analysis detected the peak area volumes of Paraquat in soil samples at level ranging from 0.3 to 5.9 mg/l. The concentration of Paraquat in sandy clay loam soil showed that herbicide leached downward to the depth of 15 cm. The maximum concentration of Paraquat residue was detected at 0 to15 cm of soil depth at 2.783 mg/l. In highly organic soils, adsorption is weaker and Paraquat remains herbicidally active for longer, up to 29 days in one trial on soil with 98% organic matter [1]. Oral dose, LD50 for human is equal to 40-60 mg/kg [2] while the lowest fatal dose recorded for human was 17 mg/kg, but even lower doses may be fatal for children [3].

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.

How this classification was reachedexpand

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.244
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Basic & Applied SciencesSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207