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Record W2067446422 · doi:10.6000/1927-5129.2014.10.75

Removal of Pesticide Residues from Tomato and its Products

2014· article· en· W2067446422 on OpenAlexvenueno aff
Aasia Akbar Panhwar, Saghir Ahmed Sheikh, Aijaz Hussain Soomro, Ghulam Hussain Abro

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsEndosulfanBifenthrinPesticidePesticide residueChemistryDehydrationImidaclopridToxicologyFood scienceAgronomyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Plant protection agents (more commonly known as pesticides) are widely used in agriculture to increase the yield, improve the quality and extend the storage life of food crops. The study was carried out in order to determine the effectiveness of various traditional processing treatments on reducing the residual load of pesticides from tomato and its products. Results showed that lipid soluble pesticides residues were reduced most effectively in sun-drying (90-97%) followed by frying (91-99%) and thermal dehydration (89-90%). The data further indicated that profenofos residues dislodged more effectively than bifenthrin and endosulfan. The least reduction was noticed in endosulfan residues. Similarly in case of water soluble pesticides, the effect of sun-drying, frying and thermal dehydration on reduction of pesticide residues were within the range of 94-97%, 92-96% and 91-96%, respectively. Maximum reduction was found in emamectin benzoate residues followed by imidacloprid and diafenthiuron.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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 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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Basic & Applied SciencesSame topicPesticide Residue Analysis and SafetyFrench-language works237,207