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Record W2210256530 · doi:10.1139/gen-2015-0026

Screening and incorporation of rust resistance from <i>Allium cepa</i> into bunching onion (<i>Allium fistulosum</i>) via alien chromosome addition

2015· article· en· W2210256530 on OpenAlexvenueno aff
Tadayuki Wako, Kenichiro Yamashita, Hikaru Tsukazaki, Takayoshi Ohara, Akio Kojima, Shigenori Yaguchi, Satoshi Shimazaki, Naoko Midorikawa, Takako Sakai, Naoki Yamauchi, Masayoshi Shigyo

Bibliographic record

VenueGenome · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsAllium fistulosumAlliumBiologyBulbRust (programming language)CultivarLiliaceaeBotanySeedlingHorticulture

Abstract

fetched live from OpenAlex

Bunching onion (Allium fistulosum L.; 2n = 16), bulb onion (Allium cepa L. Common onion group), and shallot (Allium cepa L. Aggregatum group) cultivars were inoculated with rust fungus, Puccinia allii, isolated from bunching onion. Bulb onions and shallots are highly resistant to rust, suggesting they would serve as useful resources for breeding rust resistant bunching onions. To identify the A. cepa chromosome(s) related to rust resistance, a complete set of eight A. fistulosum - shallot monosomic alien addition lines (MAALs) were inoculated with P. allii. At the seedling stage, FF+1A showed a high level of resistance in controlled-environment experiments, suggesting that the genes related to rust resistance could be located on shallot chromosome 1A. While MAAL, multi-chromosome addition line, and hypoallotriploid adult plants did not exhibit strong resistance to rust. In contrast to the high resistance of shallot, the addition line FF+1A+5A showed reproducibly high levels of rust resistance.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.197
Teacher spread0.179 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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