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Record W4394815742 · doi:10.5376/cgg.2024.15.0001

GWAS Revealed the Key Genetic Factors Affecting Cotton Fiber Quality

2024· article· en· W4394815742 on OpenAlex
Youqing Wu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCotton Genomics and Genetics · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicResearch in Cotton Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsGenome-wide association studyKey (lock)FiberQuality (philosophy)BiologyGeneticsMaterials scienceSingle-nucleotide polymorphismEcologyGeneGenotypeComposite materialPhysics

Abstract

fetched live from OpenAlex

Cotton fiber quality is an important factor in determining the economic value of cotton, mainly including fiber length, strength, fineness, maturity and other indicators. genome-wide association study (GWAS) revealed several key genetic factors affecting cotton fiber quality. It provides an important basis for the application of molecular marker-based assisted breeding and gene editing technology. This study mainly discusses the application of GWAS in revealing the key genetic factors affecting cotton fiber quality, and summarizes the basic principles and methods of GWAS in the study of cotton fiber quality by analyzing the genetic regulation mechanism of cotton fiber development and the history of variety improvement. This study explores the future direction of cotton fiber quality improvement, and emphasizes the importance of in-depth study of genetic factor function and application of new technologies.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.397

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 head0.063
GPT teacher head0.304
Teacher spread0.241 · 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