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Record W2944572649 · doi:10.5539/ijb.v11n3p10

Antimicrobial Activity of Arugula (Eruca Sativa) Leaves on Some Pathogenic Bacteria

2019· article· en· W2944572649 on OpenAlexvenueno aff
Samar Sabri Qaddoumi, Nasser El-Banna

Bibliographic record

VenueInternational Journal of Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsnot available
Fundersnot available
KeywordsErucaBacillus cereusErwiniaAntimicrobialBiologyEscherichia coliPathogenic bacteriaBacteriaStaphylococcus aureusFood scienceMicrobiologyBotanyBiochemistry

Abstract

fetched live from OpenAlex

Arugula (Eruca sativa) is a green leafy vegetable; whose flowers, seed pods and seeds are edible. It’s packed with vital nutrients that can help you step up your health without spending too much money. This study aims to fight pathogenic bacteria whether they affect plants or humans by stopping their growth and work as antibiotics. In the present study, water extract of Arugula leaves was effective against Escherichia coli HAS 11 (19mm) and Staphylococcus aureus HAS 1 (12mm), but no activity was observed against Erwinia amylovora HAS 12 and Bacillus cereus HAS 2. In the case of ethyl acetate extract, no antimicrobial activity against tested microorganisms, Staphylococcus aureus HAS 1, Bacillus cereus HAS 2, Escherichia coli HAS 11 and Erwinia amylovora HAS 12 was seen.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.265
Teacher spread0.256 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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