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Record W2171399222 · doi:10.5539/ijb.v6n3p46

The Effects of Anabasine and the Alkaloid Extract of Nicotiana glauca on Lepidopterous Larvae

2014· article· en· W2171399222 on OpenAlexvenueno aff
Michelle Zammit, Claire Shoemake, Everaldo Attard, Lilian M. Azzopardi

Bibliographic record

VenueInternational Journal of Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsAnabasineAlkaloidNicotianaPieris rapaeMetaboliteLepidoptera genitaliaNornicotineBotanyBiologyLarvaChemistryTraditional medicineToxicologySolanaceaeBiochemistryMedicine

Abstract

fetched live from OpenAlex

For several decades, Nicotiana glauca has been known for its content of the pyridine alkaloid, anabasine. The toxicological effects of this metabolite have been extensively studied, as opposed to its potential insecticidal activity. The anabasine content of leaves of N. glauca, collected from Malta, was 0.258 ± 0.0042% as determined by High Performance Liquid Chromatography. In the Pieris rapae larval bioassay, the median effective concentrations of anabasine and the alkaloid extract were 0.572 and 1.202 mg per larva, respectively. The presence of other interfering metabolites may have resulted in this elevated EC50 for the crude extract. Anabasine is quoted to be a very toxic alkaloid not solely to insects, but also to other animals, and its use in minute concentrations in insect traps may well prove it to be an effective natural insecticide.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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 designBench or experimental
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

Citations12
Published2014
Admission routes1
Has abstractyes

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