MétaCan
Menu
Back to cohort
Record W2468362191 · doi:10.1134/s1022795411120027

Genetic diversity in basmati rice (Oryza sativa L.) germplasm as revealed by microsatellite (SSR) markers

2012· article· en· W2468362191 on OpenAlexaff
Muhammad Ashfaq, Ahmad Sattar Khan

Bibliographic record

VenueRussian Journal of Genetics · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyGermplasmMicrosatelliteGenetic diversityOryza sativaDendrogramIndelGeneticsGenotypeLocus (genetics)AlleleGenetic markerPlant breedingHorticultureSingle-nucleotide polymorphismGenePopulation

Abstract

fetched live from OpenAlex

Genetic diversity among rice genotypes, including 15 indica basmati advance lines and 5 basmati improved varieties were investigated by 28 SSR markets including one indel marker. The SSRs covered all the 12 chromosomes that distributed across the rice genomes. The mean number of alleles per locus was 3.60, showing average number of polymorphism information content was 0.48. A total of 101 alleles were also identified from the microsatellite marker loci. A number of SSR markers were also identified that could be utilized to differentiate between rice genotypes. Pair wise Nei,s genetic distance between rice genotypes ranged from 0.07 to 0.95. The dendrogram based on cluster analysis by using SSR polymorphism that grouped the 20 genotypes of rice in to five clusters based on their genetic similarity. The result could be useful for the identification and selection of the diverse genotypes for the future cross breeding program and development of new rice varieties.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueRussian Journal of GeneticsSame topicRice Cultivation and Yield ImprovementFrench-language works237,207