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Record W2154868446 · doi:10.1139/g2012-034

Association mapping of height and maturity across five environments using the sorghum mini core collection

2012· article· en· W2154868446 on OpenAlexvenueno aff
Hari D. Upadhyaya, Yihong Wang, Shivali Sharma, Sube Singh

Bibliographic record

VenueGenome · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySorghumMaturity (psychological)Sweet sorghumCropGenotypingAgronomyBiotechnologyGeneticsGeneGenotype

Abstract

fetched live from OpenAlex

Sorghum is a potential energy crop thanks to its high biomass productivity and low input. Biomass yield in sorghum is defined by height and maturity. To develop molecular breeding tools for genetic improvement of these two traits, we have identified simple sequence repeat markers linked to height and maturity using a pool-based association mapping technique. The sorghum mini core collection was evaluated across five environments for height and maturity. Seven tall and seven short accessions were selected based on their height in all environments. Likewise, six early- and 10 late-maturing accessions were selected mostly based on their maturity in two post-rainy seasons. Two additional height pools were constructed based on phenotypes in one environment. The three pairs of pools were screened with 703 SSR markers and 39 polymorphic markers were confirmed by individual genotyping. Association mapping of the 39 markers with 242 accessions from the mini core collection identified five markers associated with maturity or height. All were clustered on chromosomes 6, 9, and 10 with previously mapped height and maturity markers or QTLs. One marker associated with both height and maturity was 84 kb from recently cloned Ma1. These markers will lay a foundation for identifying additional height and maturity genes in sorghum.

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)0.000
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 score0.873
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.238
Teacher spread0.216 · 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

Citations39
Published2012
Admission routes1
Has abstractyes

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