Essential Oil Composition, Antimicrobial Potential, Phytochemical Profile and Toxicity of Essential Oils and Crude Extracts of Sweet Basil Prior and After Flowering
Bibliographic record
Abstract
International Journal of Biotechnology for Wellness Industries :: Volume 1 Number 1 :: Reviews Biotechnology for Wellness Industry: Concepts and Biofactories Mohamad R. Sarmidi and Hesham A. El Enshasy Cell-Based Assays in High-Throughput Screening for Drug Discovery Ru Zang, Ding Li, I-Ching Tang, Jufang Wang and Shang-Tian Yang Applications of Polyhydroxyalkanoates in the Medical Industry Christopher J. Brigham and Anthony J. Sinskey Research Papers Mass Spectrometry Imaging of the Capsaicin Localization in the Capsicum Fruits Shu Taira, Shuichi Shimma, Issey Osaka, Daisaku Kaneko, Yuko Ichiyanagi, Ryuzo Ikeda, Yasuko Konishi-Kawamura, Shu Zhu, Koichi Tsuneyama and Katsuko Komatsu Study of Corylus cornuta Twig Extracts: Antioxidant, Radical Scavenging, Anti-Enzymatic Activities and Cytotoxicity Mariana Royer and Tatjana Stevanovic Essential Oil Composition, Antimicrobial Potential, Phytochemical Profile and Toxicity of Essential Oils and Crude Extracts of Sweet Basil Prior and After Flowering Oumadevi Rangasamy, Mohamad Fawzi Mahomoodally, Anwar Hussein Subratty and Ameenah Gurib-Fakim Effect of Initial Sugar Concentration on the Production of L (+) Lactic Acid by Simultaneous Enzymatic Hydrolysis and Fermentation of an Agro-Industrial Waste Product of Pineapple (Ananas comosus) Using Lactobacillus casei Subspecies rhamnosus Carla Araya-Cloutier, Carolina Rojas-Garbanzo and Carmela Velázquez-Carrillo Short Communication Microbial Hydroxylation of Yohimbine Hanan G. Sary and Khaled Y. Orabi
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".