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Record W2560223350 · doi:10.5650/jos.ess16080

Bioactivities and Chemical Constituents of Essential Oil Extracted from <i>Artemisia anethoides</i> Against Two Stored Product Insects

2016· article· en· W2560223350 on OpenAlexaff
Junyu Liang, Wenting Wang, Yanfei Zheng, Di Zhang, Junlong Wang, Shanshan Guo, Wenjuan Zhang, Shu-Shan Du, Ji Zhang

Bibliographic record

VenueJournal of Oleo Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsScience North
FundersFundamental Research Funds for the Central UniversitiesBeijing Normal University
KeywordsLasioderma serricorneEssential oilDEETToxicologyChemical constituentsChemistryFood scienceTraditional medicineBiologyBotanyLarvaMedicineChromatography

Abstract

fetched live from OpenAlex

The chemical constituents of the essential oil extracted from Artemisia anethoides and the bioactivities of essential oil against Tribolium castaneum and Lasioderma serricorne were investigated. The main components of the essential oil were 1,8-cineole (36.54%), 2-isopropyl-5-methyl-3-cyclohexen-1-one (10.40%), terpinen-4-ol (8.58%), 2-isopropyltoluene (6.20) and pinocarveol (5.08%). The essential oil of A. anethoides possessed contact and fumigant toxicities against T. castaneum adults (LD50 = 28.80 μg/adult and LC50 = 13.05 mg/L air, respectively) and against L. serricorne (LD50 = 24.03 μg/adult and LD50 = 8.04 mg/L air, respectively). The crude oil showed repellent activity against T. castaneum and L. serricorne. Especially, the percentage repellency of essential oil was same level with DEET (positive control) against T. castaneum. The results indicated that the essential oil of A. anethoides had the potential to be developed as insecticide and repellent for control of T. castaneum and L. serricorne.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations42
Published2016
Admission routes1
Has abstractyes

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