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Record W2613315465 · doi:10.5539/jas.v9n6p92

Pearl Millet: A Green Bridge for Lepidopteran Pests

2017· article· en· W2613315465 on OpenAlexvenueno aff
Bruna Magda Favetti, Thaís Lohaine Braga-Santos, Angélica Massarolli, Alexandre Specht, Alessandra Regina Butnariu

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
FundersUniversidade do Estado de Mato Grosso
KeywordsBiologyPupaCaterpillarCropAgronomyStrawHectareLarvaHorticultureVeterinary medicineBotanyAgricultureEcology

Abstract

fetched live from OpenAlex

This study evaluated the occurrence of lepidopteran pests on millet cultivated in off-season in the state of Mato Grosso, Brazil. Larvae were collected from May to July 2013 in an area of 145 hectares located in Tangará da Serra, MT. After being collected, caterpillars were kept in the laboratory and fed an artificial diet until the pupal stage. After emergence, adults were dry mounted, identified, and deposited in the entomological collection of Embrapa Cerrados, Planaltina, Distrito Federal, Brazil. Adults obtained from 117 caterpillars were identified as Mocis latipes (Guenée), Spodoptera frugiperda (J.E. Smith), Helicoverpa armigera (Hübner), H. zea (Boddie), Mythimna (Pseudaletia) sequax Franclemont, Urbanus proteus (Linnaeus), and Leucania latiuscula Herrich-Schäffer. This study describes the first record of lepidopteran pests on millet plants in the state of Mato Grosso, and the incidence of lepidopterans in the system that uses millet as cover crop represents a risk of the occurrence of insect pests on subsequent crops on the straw of this grass.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

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)0.0040.000

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 head0.037
GPT teacher head0.262
Teacher spread0.225 · 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 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

Citations23
Published2017
Admission routes1
Has abstractyes

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