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Record W3185463826 · doi:10.5376/ijh.2021.11.0002

Exogenous Polyazole (PP<sub>333</sub>) Regulated Flower Physiology to Promote Early Bud Extraction of Pisang Awak (ABB)

2021· article· en· W3185463826 on OpenAlexvenueno aff
He HaiWang, Peng Wu, Fang Long, Yu Zou, Tianli Mo, Xiang Huang

Bibliographic record

VenueInternational Journal of Horticulture · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyNitrogenBotanyExtraction (chemistry)BudHorticultureChemistryChromatography

Abstract

fetched live from OpenAlex

In this study, the first and second harvesting Pisang Awak in the field were sprayed with different dosage of paclobutrazol in order to analyze the mechanism of its induction of flowering. The results showed that there was no significant difference in the total number of newly extracted leaves between treatments. The first harvesting was 3.0 g/plant, and the second one was 2.0 g/plant. The pumping speed of leaves is the fastest and the accumulation of leaves number is the earliest. The 3.0 g/plant treatment of first harvesting Pisang Awak had the earliest bud extraction stage, and the bud extraction rate reached 62.5% at 170 days after treatment, about 40 days earlier than control group. Within the range of 1.0~5.0 g/plant, PP 333  promoted carbohydrate synthesis in leaves of Pisang Awak, significantly reduced the accumulation of nitrogen, significantly increased the carbon-nitrogen ratio (C/N), and significantly reduced the contents of GA 3  and IAA in the leaves. The results showed that the exogenous polyazole could accelerate the pumping speed of leaves, promote flowering and early bud extraction by regulating the distribution of carbon and nitrogen nutrients and the content of endogenous hormones, thus providing technical guidance for the management of early bud extraction culture of Pisang Awak.

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 score0.432
Threshold uncertainty score0.346

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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