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Record W2910745433 · doi:10.5539/jas.v11n2p541

Gibberellic Acid Utilization in Seeds and Plants of Beans: Effect on Growth and Seeds Physiological Quality

2019· article· en· W2910745433 on OpenAlexvenueno aff
Lanes Beatriz Acosta Jaques, Ivan Ricardo Carvalho, Vinícius Jardel Szareski, João Roberto Pimentel, Cristian Troyjack, Simone Morgan Dellagostin, Mayara Torres Mendonça, Tiago Corazza da Rosa, Francisco Amaral Villela, Velci Queiróz de Souza, Tiago Zanatta Aumonde, Tiago Pedó

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGibberellic acidGibberellinBiologyElongationHorticultureCropDistilled waterBotanyAgronomyChemistryGermination

Abstract

fetched live from OpenAlex

The growth regulators from gibberellins, when applied on plants, might improve physic and physiological features, stimulate cell division and elongation, this study aimed at evaluating the influence of gibberellic acid doses applied exogenously on bean crop through seed treatment, and analyzing its effects on morphological attributes of seedlings and physiological quality of the seeds produced. The experimental design was completely randomized design, with treatments corresponding to the doses: zero (distilled water only), 50, 100 and 200 mg L-1, arranged in four replicates. The use of gibberellic acid in bean seeds did not result in changes on the number of leaves, leaf area, dry mass of leaves and stem. Applications of foliar gibberellic acid potentiated stem height and root dry mass of seedlings.

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 score0.002
Threshold uncertainty score0.005

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 head0.027
GPT teacher head0.257
Teacher spread0.230 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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