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Record W4409751366 · doi:10.56557/upjoz/2025/v46i94931

Effect of Seasonal Incidence of Major Insect Pests of Mustard Crop in Relation to Abiotic Factors

2025· article· en· W4409751366 on OpenAlexaboutno aff
Ankit Upadhyay, Subham Kumar, Vivek Singh

Bibliographic record

VenueUTTAR PRADESH JOURNAL OF ZOOLOGY · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsAbiotic componentCropBiologyInsectAgronomyIncidence (geometry)Insect pestToxicologyEcologyMathematics

Abstract

fetched live from OpenAlex

Rapeseed-mustard is a key oilseed crop in India, contributing 11% to global production, ranking third after Canada and China. However, mustard crops face threats from over several insect pests, including aphids, sawflies, painted bugs, leaf webbers, and leaf miners. A field study conducted in Kanpur during the 2021-22 and 2022-23 Rabi seasons examined the seasonal incidence of these pests and their correlation with weather parameters. Mustard aphid infestations peaked in February, with population fluctuations negatively correlated with temperature. Similarly, the painted bug infestation peaked in December-January, with significant negative correlations with maximum and minimum temperatures. The mustard sawfly and leaf miner also showed seasonal fluctuations influenced by temperature and humidity. Their populations varied seasonally, with correlations to temperature, humidity, and rainfall.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.247
Teacher spread0.238 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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