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Record W3022220251 · doi:10.1139/cjps-2019-0231

Salt-tolerance identification and quality evaluation of <i>Abelmoschus manihot</i> (L.) Medik

2020· article· en· W3022220251 on OpenAlexvenueno aff
Zhe Wu, Zhizhong Xue, Xuelin Lu, Yinsuo Jia, Xiuping Wang, Xiaodong Zhang

Bibliographic record

VenueCanadian Journal of Plant Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsManihotHyperosidePhytochemicalHorticultureBotanyChemistryAgronomyBiologyRutin

Abstract

fetched live from OpenAlex

Abelmoschus manihot (L.) Medik. is a medicinal and edible plant. To evaluate its suitability for cultivation on the coastal saline-alkali land in northern China for high quality functional products, salt-tolerance identification and flavonoid contents were evaluated under saline treatments. Results showed that the salt-tolerance threshold of A. manihot ranged from 4.1 to 6.9 g L−1; however, low soil salt content (<3 g L−1) had the best growth and accumulation of total flavonoids. Sixteen kinds of common functional components such as hyperoside, rutoside, and quercetin were found. Of these components, the four (myricetin-3-0-glucoside, rutoside, quercetin-3′-0-glucoside, and gossypetin-8-0-β-d-glucuronic acid) with the highest content were chosen as the quality evaluation indexes. High levels of quality and yield occurred at a soil salt content of 3 g L−1. Our results suggested that soil salt content should not exceed 3 g L−1 in field cultivation for high quality and high yield of A. manihot.

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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.090
GPT teacher head0.274
Teacher spread0.184 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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