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Record W2783195262 · doi:10.5539/jas.v10n2p187

Interrelations of Characters and Multivariate Analysis in Corn

2018· article· en· W2783195262 on OpenAlexvenueno aff
Vinícius Jardel Szareski, Ivan Ricardo Carvalho, Kassiana Kehl, Alan Júnior de Pelegrin, Maicon Nardino, Gustavo Henrique Demari, Maurício Horbach Barbosa, Francine Lautenchleger, Djonimar Smaniotto, Tiago Zanatta Aumonde, Tiago Pedó, Velci Queiróz de Souza

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsHybridRandomized block designGrain yieldMultivariate statisticsYield (engineering)BiologyCropAgronomyMathematicsCrop yieldStatisticsHorticulture

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the agronomic performance of corn hybrids, the interrelations of the characters with the grain yield, and to genetically discriminate the corn hybrids by means of the dispersion analysis of the canonical variables. The experiment was conducted in the agricultural crop of 2013/2014, in an area belonging to the Federal University of Santa Maria, Campus of Frederico Westphalen, RS. The experimental design was a randomized block design, with four replications. The treatments were composed of seven maize hybrids with different genetic bases and maturation cycles. The LG 6304 modified simple hybrid has higher grain yield than the others. The characters plant height, spike insertion height and number of grains per row of spike have positive interrelations with grain yield of corn hybrids. Hybrids are not grouped according to the genetic basis and maturation cycle. The canonical variables explain 94.62% of the existing genetic variation, and allows the formation of five groups of maize hybrids.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.071

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 studies0.0000.000
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 head0.024
GPT teacher head0.232
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.

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

Citations21
Published2018
Admission routes1
Has abstractyes

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