MétaCan
Menu
Back to cohort
Record W2914575671 · doi:10.1111/pbr.12679

Density enhancement of a faba bean genetic linkage map (<i>Vicia faba</i>) based on simple sequence repeats markers

2019· article· en· W2914575671 on OpenAlexaff
Tao Yang, Junye Jiang, Hongyan Zhang, Rong Liu, Stephen E. Strelkov, Sheau‐Fang Hwang, K. F. Chang, Feng Yang, Yamei Miao, Yuhua He, Xuxiao Zong

Bibliographic record

VenuePlant Breeding · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
FundersAgriculture Research System of ChinaChinese Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsVicia fabaBiologyGenetic linkageGeneticsGenetic markerLinkage (software)MicrosatelliteGene mappingGenetic linkage mapAlleleBotanyGeneChromosome

Abstract

fetched live from OpenAlex

Abstract Genetic mapping for faba bean lags far behind other major crops. Density enhancement of the faba bean genetic linkage map was carried out by screening 5,325 genomic SSR primers and 2033 expressed sequence tag (EST)‐SSR primers on the parental cultivars '91825' and 'K1563'. Two hundred and fifteen genomic SSR and 133 EST‐SSR primer pairs that detected polymorphisms in the parents were used to screen 129 F 2 individuals. This study added 337 more SSR markers and extended the previous linkage map by 2928.45 cM to a total of 4516.75 cM. The number of SSR markers in the linkage groups varied from 12 to 136 while the length of each linkage group ranged from 129.35 to 1180.21 cM. The average distance between adjacent loci in the enhanced genetic linkage map was 9.71 cM, which is 2.79 cM shorter than the first linkage map of faba bean. The density‐enhanced genetic map of faba bean will be useful for marker‐assisted selection and breeding in this important legume crop.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.320

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 head0.024
GPT teacher head0.197
Teacher spread0.174 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePlant BreedingSame topicGenetic and Environmental Crop StudiesFrench-language works237,207