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Record W2029204500 · doi:10.1139/g07-106

Identification of stably expressed quantitative trait loci for cooked rice elongation in non-Basmati varieties

2008· article· en· W2029204500 on OpenAlexvenueno aff
Lanlan Liu, Xin Yan, Ling Jiang, Wei‐Lin Zhang, M. Q. Wang, Shirong Zhou, Yingchao Shen, Yiwen Shen, Shuchen Liu, Liang-Ming CHEN, Jou‐Kou Wang, Jianmin Wan

Bibliographic record

VenueGenome · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaChengdu University of Information Technology
KeywordsBiologyQuantitative trait locusJaponicaInbred strainChromosomeGeneticsElongationJaponica riceGeneBotany

Abstract

fetched live from OpenAlex

The elongation of the cooked grain determines the cooking and eating quality of Basmati rice. The identification of stable quantitative trait loci (QTLs), especially those from non-Basmati types, will extend the genetic basis of the Basmati type and facilitate the breeding of high-quality varieties. A set of recombinant inbred lines derived from an indica x japonica hybrid was used to identify QTLs controlling the elongation ratio (ER), elongation index (EI), and water absorption (WA) of the cooked grain. Three ER QTLs on chromosomes 2, 4, and 12, two EI QTLs on chromosomes 2 and 5, and two WA QTLs on chromosomes 2 and 6 were detected. Four of these QTLs were validated using a set of established chromosome segment substitution lines. The genetic effect of qER-2 was explored in an analysis of segregating generations, using 8 newly developed simple sequence repeat markers. Two tightly linked loci (qER-2a and qER-2b) were identified on chromosome 2.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.023
GPT teacher head0.242
Teacher spread0.220 · 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 designObservational
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

Citations12
Published2008
Admission routes1
Has abstractyes

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