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Record W4391782527 · doi:10.53555/sfs.v10i1s.2286

A Study On Bio Efficacy Of Cyantraniliprole 10.6% OD Against Major Sucking Pests Of Watermelon

2023· article· en· W4391782527 on OpenAlexvenueno aff
Amit Layek, Kaushik Pramanik, Rakesh Das, Debarati Seal

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Practices and Plant Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyToxicologyMedicineBiotechnologyTraditional medicine

Abstract

fetched live from OpenAlex

Cyantraniliprole, 10.6% OD, was evaluated in the field against major sucking pests of watermelon at Jaguli Instructional Farm, Bidhan Chandra Krishi Viswavidyalaya (BCKV) during 2020-21. The study took place during the kharif season, and the watermelon variety named ‘Krishna’ was selected and grown with proper agronomic practices. A total of three sprays were done starting from the ETL of the pest population. The spraying was done at ten days intervals with four different doses of Cyantraniliprole, 10.6% OD along with Imidacloprid 17.8% SL @ 100 g a.i./ha, Spinosad 45% SC @ 73 g a.i./ha (standard checks) and an untreated control. All the treatment is arranged in randomized block design with three replications, each consisting of seven treatments. The results revealed that treatments cyantraniliprole 10.6% OD @ 120 g a.i/ha and 90 g a.i/ha were substantially more potent with regard to its efficacy on the basis of three sprays, compared to untreated control.

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 score0.003
Threshold uncertainty score0.006

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 head0.200
GPT teacher head0.285
Teacher spread0.084 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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