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Record W4293659993 · doi:10.5376/ijh.2022.12.0002

Research Progress on Waterlogging Tolerance of <i>Cucurbit</i> Crops

2022· article· en· W4293659993 on OpenAlexvenueno aff
Jiawen Zheng, Yong He

Bibliographic record

VenueInternational Journal of Horticulture · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsnot available
Fundersnot available
KeywordsWaterlogging (archaeology)CultivarPhotosynthesisBiologyAgronomyHorticultureBotanyEcologyWetland

Abstract

fetched live from OpenAlex

As one of the most frequent natural disasters in China, flooding seriously affected the plant growth and development, decreased the yield and quality of  Cucurbit crops seriously, and thus led to huge economic losses. Here, we reviewed the effects of waterlogging stress on root growth, respiration, leaf photosynthesis and reactive oxygen species metabolism in  Cucurbit  crops. In order to response to waterlogging, the plants promote the formation of adventitious roots mediated by ethylene, lower the aerobic respiration, and induce the activity of antioxidant enzymes and the synthesis of antioxidant substances. Moreover, we summarized the identification indexes of waterlogging tolerance such as the loss rate of chlorophyll. In addition, we reviewed the progress of genetic breeding for waterlogging tolerant cultivars from three aspects, including traditional breeding, molecular marker-assisted breeding and genetic engineering breeding. Besides, we highlighted the future research direction. The purpose of this review was to understand the mechanism of waterlogging tolerance and to provide a theoretical basis for the breeding of waterlogging resistant cultivars in  Cucurbit  plants.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.286
Teacher spread0.263 · 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
Published2022
Admission routes1
Has abstractyes

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