MétaCan
Menu
Back to cohort
Record W4413070078 · doi:10.5376/mpr.2024.14.0029

Screening of Disease and Pest Resistant Hangbaiju (<i>Chrysanthemum morifolium</i>) Varieties and Their Application Prospects in Green Cultivation

2024· article· en· W4413070078 on OpenAlexvenueno aff
Jianli Lu, Chuchu Liu

Bibliographic record

VenueMedicinal Plant Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsChrysanthemum morifoliumPEST analysisDisease resistantHorticultureBiologyPlant disease resistance

Abstract

fetched live from OpenAlex

Hangbaiju (Chrysanthemum morifolium) is popular among consumers because of its anti-inflammatory, antioxidant and liver-protecting effects, and it has both medicinal and ornamental value.With the expansion of planting scale, pests and diseases have become the main obstacles affecting yield and quality.Although chemical pesticides are still the main means of prevention and control, there are problems such as increased resistance and environmental pollution.In order to promote green planting, this study screened out a number of resistant chrysanthemum varieties, and combined field natural infection experimental analysis, artificial inoculation experiments and molecular marker analysis to clarify the role of resistance genes, like CmWD40 and CmWRKY8-1 in the defense mechanism.The results showed that, these varieties can effectively reduce the incidence of pests and diseases while maintaining yield and quality, and adapt to the green prevention and control system.This study provides strong germplasm support and theoretical basis for the sustainable cultivation of Hangbaiju, and helps its promotion and application in ecological agriculture and organic certification paths.

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 score0.881
Threshold uncertainty score0.437

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 science0.0000.000
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 head0.022
GPT teacher head0.283
Teacher spread0.260 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMedicinal Plant ResearchSame topicPlant Pathogens and Fungal DiseasesFrench-language works237,207