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Record W2171543377 · doi:10.4039/n05-038

Simple technique to increase the sensitivity of probe traps in detecting <i>Cryptolestes ferrugineus</i> (Coleoptera: Laemophloeidae) in stored wheat

2006· article· en· W2171543377 on OpenAlexaff
S. Mohan, Sethuraman Sivakumar, S.R. Venkatesh, Vijaya Raghavan

Bibliographic record

VenueThe Canadian Entomologist · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsMcGill University
Fundersnot available
KeywordsTrap (plumbing)Wheat grainWheat flourBiologyPEST analysisAgronomyHorticultureFood scienceEnvironmental science

Abstract

fetched live from OpenAlex

Abstract An approach to enhance the performance of probe traps in detecting Cryptolestes ferrugineus (Stephens) adults in stored wheat is described. The repellent property of protein-enriched pea flour is exploited to increase the efficiency of the probe trap by treating the stored grain with the flour. The enhancement in performance of the traps was evaluated by placing the traps in wheat grains treated with protein-enriched pea flour at concentrations of 1%, 5%, and 10% ( w/w ) and observing the number of adult beetles trapped in comparison with the trap catch in untreated wheat. The traps kept in wheat treated with protein-enriched pea flour caught more beetles than traps placed in untreated wheat. The practical implications of this work are discussed with reference to sampling for C. ferrugineus .

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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
Published2006
Admission routes1
Has abstractyes

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