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Record W2947764579 · doi:10.5539/jas.v11n8p236

Color Shade Heritability of Peach Flesh

2019· article· en· W2947764579 on OpenAlexvenueno aff
Maximiliano Dini, Maria do Carmo Bassols Raseira, Silvia Scariotto, Bruno Carra, Everton Sozo de Abreu, Paulo Mello-Farias, R. F. Flores Cantillano

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAgencia Nacional de Investigación e Innovación
KeywordsHueHeritabilityLightnessFleshBiologyHorticultureCropMathematicsBotanyAgronomyArtificial intelligenceComputer scienceEvolutionary biology

Abstract

fetched live from OpenAlex

Nowadays, despite of being a typically temperate fruit crop, peach is found in subtropical and even tropical altitude zones, due to breeding efforts. Genetic knowledge and phenotypic and environmental parameters directly or indirectly influence the characters of economic importance in peach, and they are of great importance for breeding programs orientation. The aim of this study was to estimate heritability of a color shade character of peach flesh, measured by the Hue angle, chroma and lightness to evaluate its distribution in the populations, testing the possible existence of maternal effect. Results showed that parameters related to color shade of peach flesh have a medium heritability. Parents selection based on phenotype allows a medium genetic improvement for the mentioned character. This heritability is predominantly additive, without maternal effect. The Hue angle is the correct parameter to be used to classify and study yellow-flesh peaches and nectarines in relation to their color shade. However, the multivariate analysis of principal components, using the three parameters Hue angle, chroma and lightness, is an alternative of high accuracy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.220
Teacher spread0.205 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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