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Record W2907251536 · doi:10.5539/jas.v11n2p169

Improvement of Cymbopogon flexuosus Biomass and Essential Oil Production With Organic Manures

2019· article· en· W2907251536 on OpenAlexvenueno aff
V. R. de O. Lopes, Suzan Kelly Vilela Bertolucci, Alexandre Alves de Carvalho, Heitor Luiz Heiderich Roza, Felipe Campos Figueiredo, José Eduardo Brasil Pereira Pinto

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsnot available
Fundersnot available
KeywordsCompostManureNutrientEssential oilAgronomyQuailBiomass (ecology)BiologyFertilizerChemistryBotanyEcology

Abstract

fetched live from OpenAlex

Cymbopogon flexuosus is a medicinal species with relevant commercial value and widely used in flavors, fragrances, toiletries, cosmetics, soaps, detergents, and pharmaceutical products. The objective of this study was evaluate the effects of different doses and sources of two manures and one compost on plant growth, leaf nutrient accumulation, content, yield, and chemical composition of the essential oil. The sources were cattle manure, quail manure, and organic compost applied in four doses and control treatment. The species increased the growth and productions of secondary metabolites by use the manures and organic compost. The highest weight gain was obtained with fertilization between 300 and 450 g pot-1 of quail manure, but the yield of essential oil in the doses from 150 to 300 g pot-1. Overall, lemon grass plants fertilized with quail manure accumulated greater concentrations of nutrients in leaf tissue than cattle manure and compost. The neral content in the essential oil was increased with the use of quail manure compared to compost and cattle manure. The results from this study demonstrated that dose and source can alter dry weight, leaf nutrients accumulation, and essential oil content and yield.

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 score0.282
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

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