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Record W4415500691 · doi:10.25181/jppt.v25i1.3554

Penentuan Jenis Bahan Aktif Insektisida Dalam Pengendalian Spodoptera exigua Menggunakan Bioassay Tanpa Rearing

2025· article· W4415500691 on OpenAlexaff
Catur Raharjo Febrayanto, Fitri Susiyanti, Koko Dwi Sutanto, Arina Maulinda, Adiba Eva Perdani, Didi Carsidi, Bagus Nur Rochman, Muhammad Bachtiar Musthafa

Bibliographic record

VenueJurnal Penelitian Pertanian Terapan · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicAgricultural Research and Practices
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsExiguaLarvaSpodopteraNovaluron

Abstract

fetched live from OpenAlex

Spodoptera exigua adalah salah satu hama utama tanaman bawang merah yang sulit dikendalikan oleh petani bawang merah di Kabupaten Brebes. Pengendalian S. exigua pada umumnya menggunakan insektisida kimia. Penelitian ini bertujuan untuk untuk menguji efikasi insektisida terhadap larva S. exigua dengan metode bioassay tanpa rearing, mengetahui mortalitas larva S. exigua terhadap insektisida, dan mengetahui kecenderungan terjadinya cross resistance pada larva S. exigua. Penelitian ini menggunakan paradigma kuantitatif dengan experimental Design. Jenis insektisida yang digunakan terdiri dari 7 Merek dagang yang mengandung bahan aktif berbeda dan satu kontrol yang diujikan ke larva S. exigua dari Desa Kedunguter, Kecamatan Brebes, Kabupaten Brebes. Analisis data menggunakan analisis univariat, dilanjutkan dengan uji Duncan’s Multiple Range Test dan uji hierarchical cluster. Hasil penelitian menunjukan bahwa metode bioassay tanpa rearing dapat digunakan untuk menentukan jenis insektisida yang akan digunakan untuk pengendalian S. exigua. Mortalitas larva S. exigua yang bervariasi terhadap insektisida. Mortalitas terendah terjadi pada Merek A, Merek C dan Merek B, sedangkan mortalitas tertinggi terjadi pada Merek F dan Merek E. Terdapat kecenderungan terjadinya cross resistance pada S. exigua antara bahan aktif abamektin, emamektin benzoat dan beta siflutrin. Insektisida yang direkomendasikan untuk digunakan dalam pengendalian larva S. exigua adalah merk F dan merk E.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.016
GPT teacher head0.279
Teacher spread0.262 · 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; both teacher heads agree on what is shown here.

Study designOther design
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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