MétaCan
Menu
Back to cohort
Record W2060832017 · doi:10.1126/science.1161847

A Physical Map of the 1-Gigabase Bread Wheat Chromosome 3B

2008· article· en· W2060832017 on OpenAlexaff
Etienne Paux, Pierre Sourdille, Jérôme Salse, Cyrille Saintenac, Frédéric Choulet, Philippe Leroy, Abraham Korol, Monika Michalak, Shahryar F. Kianian, Wolfgang Spielmeyer, Evans Lagudah, Daryl J. Somers, Andrzej Kilian, Michaël Alaux, Sonia Vautrin, Hélène Bergès, Kellye Eversole, R. Appels, Jan Šafář, Hana Šimková, Jaroslav Doležel, Michel Bernard, Catherine Feuillet

Bibliographic record

VenueScience · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsContigPolyploidChromosomeBiologyGenomeGeneticsPhysical mappingPopulationBacterial artificial chromosomeComputational biologyGene mappingGene

Abstract

fetched live from OpenAlex

As the staple food for 35% of the world's population, wheat is one of the most important crop species. To date, sequence-based tools to accelerate wheat improvement are lacking. As part of the international effort to sequence the 17-billion-base-pair hexaploid bread wheat genome (2n = 6x = 42 chromosomes), we constructed a bacterial artificial chromosome (BAC)-based integrated physical map of the largest chromosome, 3B, that alone is 995 megabases. A chromosome-specific BAC library was used to assemble 82% of the chromosome into 1036 contigs that were anchored with 1443 molecular markers, providing a major resource for genetic and genomic studies. This physical map establishes a template for the remaining wheat chromosomes and demonstrates the feasibility of constructing physical maps in large, complex, polyploid genomes with a chromosome-based approach.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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

Citations406
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicChromosomal and Genetic VariationsFrench-language works237,207