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Record W1809989152 · doi:10.5539/jas.v7n9p228

Adaptability and Stability of Corn Hybrids in Southern Brazilian Environments

2015· article· en· W1809989152 on OpenAlexvenueno aff
Carlos Busanello, Velci Queiróz de Souza, Antônio Costa de Oliveira, Maicon Nardino, Diego Baretta, Braulio Otomar Caron, Denise Schmidt, Victoria Freitas de Oliveira, Valmor Antônio Konflanz

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsHybridAdaptabilityRandomized block designGrain yieldBiologyAgronomyYield (engineering)Gene–environment interactionCropGenotypeStability (learning theory)BiotechnologyGeneticsEcologyComputer scienceGene

Abstract

fetched live from OpenAlex

The objective of this study was to identify, among the 27 hybrids evaluated, the best adaptability and stability responses for corn grain yield in five environments. The experiments were conducted in the 2009/2010 crop seasons in a randomized complete block design with three replications. There was significant G x E interaction effects (p ≤ 0.05) only for the yield and cob mass characters. Among the hybrids tested, 14 of them were ranked as responsive to environmental improvement for grain yield. The other genotypes have good performance in harsh environments. For the stability analysis, significance was found for the hybrids 5, 7 and 27. For cob mass, all hybrids showed significant adaptability, for stability, only genotypes 20 and 24 expressed significance.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.038
GPT teacher head0.209
Teacher spread0.171 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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