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Record W2810102070 · doi:10.3390/agriculture8070095

Can Parentage Analysis Facilitate Breeding Activities in Root and Tuber Crops?

2018· article· en· W2810102070 on OpenAlexfundno aff
Asrat Asfaw, Pangirayi Tongoona, Agyemang Danquah, Eric Yirenkyi Danquah, David De Koeyer, Robert Asiedu

Bibliographic record

VenueAgriculture · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsnot available
FundersUniversity of GhanaInternational Development Research CentreBill and Melinda Gates Foundation
KeywordsOutcrossingBiologyGenetic gainAgronomyBreeding programSelection (genetic algorithm)Plant breedingCropPollinationMutation breedingBiotechnologyPloidyGenetic variationCultivarBotanyGenetics

Abstract

fetched live from OpenAlex

Controlled pollination in root and tuber crops is challenging. Complex ploidy, cross-incompatibility, erratic flowering patterns, outcrossing, etc., limit the efficiency of breeding progress in these crops. Half-sib breeding that involves random pollination among parents is a viable method to harness genetic gain in outcrossing crops that are problematic for performing planned and controlled pollination. The authenticity of resulting progenies from the half-sib breeding is essential to monitor the selection gain in the breeding program. Parentage analysis facilitated by molecular markers is among the available handy tools for crop breeders to maximize genetic gain in a breeding program. It can help to resolve the identity of half-sib progenies and reconstruct the pedigree in the outcrossing crops. This paper reviews the potential benefits of parentage analysis in breeding selected outcrossing root and tuber crops. It assesses how paternity analysis facilitates breeding activities and the ways it improves genetic gain in the root and tuber breeding programs. Conscious use of complementary techniques in the root and tuber breeding programs can increase the selection gain by reducing the long breeding cycle and cost, as well as reliable exploitation of the heritable variation in the desired direction.

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 score0.618
Threshold uncertainty score0.842

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 head0.032
GPT teacher head0.249
Teacher spread0.217 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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