MétaCan
Menu
Back to cohort
Record W2518100669 · doi:10.5539/jas.v8n9p135

Stabilized Organic Matter Quantities May Strongly Affect the Mobility of Glyphosate into Soil

2016· article· en· W2518100669 on OpenAlexvenueno aff
Paulo R. Dores-Silva, Fernanda Antico Benetti, Bruno Molero da Silva, Maria Diva Landgraf, Maria Olímpia Oliveira Rezende

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsGlyphosateOrganic matterLeaching (pedology)ChemistryEnvironmental chemistryAdsorptionSoil waterHumic acidSoil organic matterNatural organic matterGlycineAgronomyEnvironmental scienceSoil scienceBiologyBiochemistryOrganic chemistryAmino acid

Abstract

fetched live from OpenAlex

Glyphosate [N-(phosphonomethyl) glycine] is an enzyme inhibiting herbicide, which is widely used in the world. Here, we investigate the glyphosate adsorption potential in tropical soils with pH close to 5. The herbicide was determined via HPLC with detection by UV-Vis. Our results suggest that glyphosate interacts especially with the stabilized organic matter. Thus, even with large amounts of soil organic matter, the soils may not be as effective in the adsorption of the herbicide if they do not also possess high percentage of humic substances. These data are worth far as it allows us to speculate on glyphosate interaction mechanism with the humic substances in slightly acid medium, in addition, also enable us to propose the use of that fraction of the organic matter to immobilize part of the herbicide in the soil and inhibiting its leaching into water bodies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207