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Record W1799198196 · doi:10.18539/cast.v19i1.2904

Parameters of adaptability and stability in brazilian and exotic cultivars of white oat

2013· article· en· W1799198196 on OpenAlexaboutno aff
Henrique de Souza Luche, Rafael Nörnberg, Maraisa Crestani Hawerroth, Guilherme Ribeiro, Leomar Guilherme Woyann, José Antônio Gonzalez da Silva, Antônio Costa de Oliveira

Bibliographic record

VenueCurrent Agricultural Science and Technology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptabilityCultivarAvenaGermplasmBiologyAgronomyGene poolHybridGenotypeAdaptation (eye)BiotechnologyHorticultureGeneGenetic diversityEcologyGenetics

Abstract

fetched live from OpenAlex

The introduction of exotic germoplasm is one of the most important tools in obtaining genetic variability in breeding programs. However, it presents problems of adaptation and stability on specific conditions of climate and soil. The objective of the study was to evaluate the performance of Brazilian and exotic genotypes of oat (Avena sativa L.) and the parameters of adaptability and stability in different years of cultivation to local conditions in Capao do Leao/RS, Brazil. Experiments were conducted in 2005, 2006 and 2007 using ten US oat cultivars, thirteen brazilian cultivars  and one canadian cultivar, in an experimental design of randomized blocks with three replications. The national usually outperformed the foreign genotypes, the latter showing difficulties in adaptation to the studied conditions. Cultivars Assiniboia and Hi-Fi had performances similar to the Brazilian elite genotypes, with adequate average production, good adaptability and phenotypic stability. The introduction of this type of germplasm may increase the gene pool with new groups of genes and/or alleles carrying genetic gains in the improvement of oats.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.215
Teacher spread0.178 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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