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Record W3127751978 · doi:10.1111/pbr.12895

Potential of rutabaga (<i>Brassica napus</i> var. <i>napobrassica</i>) gene pool for use in the breeding of hybrid spring <i>Brassica napus</i> canola

2021· article· en· W3127751978 on OpenAlexaff
Bijan Shiranifar, Neil Hobson, Berisso Kebede, Rong‐Cai Yang, Habibur Rahman

Bibliographic record

VenuePlant Breeding · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAlberta Ministry of Agriculture and ForestrySyngenta (Canada)University of Alberta
Fundersnot available
KeywordsCanolaHeterosisBrassicaBiologyHybridInbred strainPopulationAgronomyYield (engineering)Genetic diversityHorticultureGeneGenetics

Abstract

fetched live from OpenAlex

Abstract The use of rutabaga for increasing the level of heterosis in spring canola was investigated. For this, test hybrids were produced by crossing four inbred populations, derived from F 2 and BC 1 of two rutabaga × spring canola crosses, to their spring canola parent, and were tested in field‐plots for agronomic and seed quality traits. Average mid‐parent heterosis (MPH) in these test hybrid populations was about 5%‐15% for yield. Compared to the F 2 ‐derived population, the BC 1 ‐derived population exhibited 1.8‐times greater MPH for yield. No correlation was found between inbred and test hybrid for yield; however, a positive correlation of genetic diversity of the inbred lines was found with MPH ( r = .58) and hybrid yield ( r = .36) suggesting that non‐additive effect of the genes may play an important role for high yield in hybrids. About 2% negative MPH was found for days to flowering, while almost no heterosis was found for seed oil and protein contents. Thus, our study demonstrates the potential of the rutabaga gene pool for use in hybrid canola breeding.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

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