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Record W4410904739 · doi:10.5267/j.ccl.2025.2.003

Tracking movement dynamic of fenitrothion and thiobencarb in rice paddy using a field lysimeters at different levels of soil depth

2025· article· en· W4410904739 on OpenAlexvenueno aff
Mohamed R. Fouad, Ahmed F. El-Aswad, Maher I. Al

Bibliographic record

VenueCurrent Chemistry Letters · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsLysimeterChemistryFenitrothionPaddy fieldTracking (education)Movement (music)Rice plantField (mathematics)AgronomySoil scienceSoil waterPesticideEnvironmental sciencePsychology

Abstract

fetched live from OpenAlex

In this study, the movement dynamic of fenitrothion (50% EC) and thiobencarb (50% EC) was investigated using the field lysimeter in the presence of rice plant at four different levels of soil depth. Iodide was used as an indicator of the mobility of these pesticides through the soil in the field lysimeter. Iodide was detected in the leachates collected at level 1 and 2 only, the concentration of iodide collected from level 2 was more than those collected from level 1. The highest breakthrough curve for fenitrothion or thiobencarb was produced from the level 4 (deep level) followed by level 3 while the breakthrough curve of level 1 was the lowest peak. Significant differences were observed among the cumulative amounts of fenitrothion or thiobencarb collected from different depth levels. The pesticide residues in the leachates increase with the depth of soil profile increase. The cumulative amounts of the two tested pesticides were compatible with the concentration of treatments, and were higher in high-treatment (50 μg/g soil) compared with that in low-treatment (25 μg/g soil). Our results obtained leaching of thiobencarb was slightly higher than the leaching of fenitrothion. These results are useful in understanding the movement of pesticides and agrochemicals in the agricultural environment.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.277

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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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 designBench or experimental
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

Citations11
Published2025
Admission routes1
Has abstractyes

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