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Record W4225116330 · doi:10.1149/10701.3845ecst

Assessment of Organophosphate Pesticides Residue in Groundwater of Kota Region of Rajasthan, India

2022· article· en· W4225116330 on OpenAlexaff
Samrin Sheikh, Kuldeep Kuldeep, Anil K. Mathur, Pawan Kamboj

Bibliographic record

VenueECS Transactions · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsGeorgian College
Fundersnot available
KeywordsPesticideGroundwaterCreaturesEnvironmental scienceAgricultureOrganophosphatePollutionPesticide residueGroundwater pollutionEnvironmental engineeringEnvironmental chemistryWater resource managementEnvironmental protectionGeographyAquiferChemistryEngineeringEcologyNatural (archaeology)Biology

Abstract

fetched live from OpenAlex

Pesticide use in agriculture has the potential to contaminate groundwater supplies. In reality, according to studies, only about 0.1 percent of toxins widely used in agriculture reach the target insect. At the same time, the rest enters the environment inappropriately and contaminates soil, water, and air, poisoning or harming non-target creatures. Organophosphorus pesticides (OPs) were widely used when organochlorine pesticides were prohibited from using in the 1960s and 1970s due to their harmful effects. This study aimed to measure the extent of organophosphate pesticides pollution in the groundwater of the Kota region. Contaminant compounds of several organophosphate pesticides have been identified in the groundwater of the Kota region.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.997

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)0.0040.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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