MétaCan
Menu
Back to cohort
Record W1823880090 · doi:10.1139/g2012-014

A genetic linkage map of crested wheatgrass based on AFLP and RAPD markers

2012· article· en· W1823880090 on OpenAlexvenueno aff
Xiaoxia Yu, Xiaolei Li, Yanhong Ma, Zhuo Yu, LI Zao-zhe

Bibliographic record

VenueGenome · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyInner Mongolia Agricultural UniversityUniversity of California, DavisInner Mongolia University
KeywordsRAPDBiologyAmplified fragment length polymorphismGeneticsGenetic markerGenetic linkageGenetic distancePopulationGene mappingMolecular markerMicrosatelliteLinkage (software)Genetic linkage mapAgropyron cristatumGeneBotanyChromosomeGenetic variationGenetic diversityAllele

Abstract

fetched live from OpenAlex

Using a population of 105 interspecific F(2) hybrids derived from a cross between Agropyron mongolicum Keng and Agropyron cristatum (L.) Gaertn. 'Fairway' as a mapping population, a genetic linkage map of crested wheatgrass was constructed based on AFLP and RAPD molecular markers. A total of 175 markers, including 152 AFLP and 23 RAPD markers, were ordered in seven linkage groups. The map distance was 416 cM, with a mean distance of 2.47 cM between markers. The number of markers ranged from 13 to 46 in each linkage group and the length of groups ranged from 18 to 104 cM. The research found that 30 out of 175 molecular markers showed segregation distortion, accounting for 17% of all markers. This is the first genetic linkage map of crested wheatgrass. This map will facilitate gene localization, cloning, and molecular marker-assisted selection in the future.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.201
Teacher spread0.186 · 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 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

Citations17
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueGenomeSame topicWheat and Barley Genetics and PathologyFrench-language works237,207