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Early selection of full-sib potato families

2011· article· en· W2006957731 on OpenAlexaff
D. S. Melo, César Augusto Brasil Pereira Pinto, Leandro Santos Peixouto, Diogo Gonçalves Neder, Josiane Cristina de Assis

Bibliographic record

VenueCiência e Agrotecnologia · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsSeneca Polytechnic
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSelection (genetic algorithm)Solanum tuberosumBiologyRandomized block designGenetic gainClonal selectionBiotechnologyHorticultureGeneticsGenetic variationComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Clonal selection is the preferred breeding method used in potatoes (Solanum tuberosum L.). However this selection procedure is only efficient for more advanced generations and shows no good results when applied in the seedling up to the second clonal generation. This study assessed the feasibility of selection in early generations of full-sib potato families and compares the selection method among and within families with the combined selection under different selection intensities. Six experiments were conducted from the first (C1) until the third clonal generation (C3). In C1 a randomized complete block design with four replications of 25 plants was used. In the remaining generations RCB was employed with three replications of 10 plants. Genetic variances were lower between families than within families, for all traits, but the heritabilities between families were almost always larger. The expected gains from selection between and within families were superior to gains from the combined selection in any intensity of selection. The selection of families should have weaker intensity than selection among clones within families. The selection of families was more efficient when based on the average of environments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.186
Teacher spread0.164 · 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 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

Citations17
Published2011
Admission routes1
Has abstractyes

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