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Record W2982440121 · doi:10.3390/agronomy9110704

Domestication in Real Time: The Curious Case of a Trigenomic Sunflower Population

2019· article· en· W2982440121 on OpenAlexfundno aff
Jill M. Ekar, Kevin J. Betts, Adam Herman, Robert M. Stupar, Donald L. Wyse, Yaniv Brandvain, Michael B. Kantar

Bibliographic record

VenueAgronomy · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSunflower and Safflower Cultivation
Canadian institutionsnot available
FundersHatchMinnesota Department of Agriculture
KeywordsIdeotypeBiologyDomesticationPerennial plantHelianthus annuusPopulationHelianthusSunflowerInterspecific competitionAgronomyBotanyCropEcology

Abstract

fetched live from OpenAlex

This study chronicles the ongoing process to domesticate an interspecific trigenomic tetraploid hybrid sunflower derived from a series of interspecific crosses between Helianthus annuus and Helianthus tuberosus. The goal of this process is to develop a perennial oilseed crop that can produce both high value vegetable oil and continuous ground-cover. Selection has focused on developing an ideotype with the domesticated morphology of H. annuus and the below-ground perennial features of H. tuberosus. The overarching challenge in the process of breeding and domesticating this interspecific perennial sunflower is overcoming obstacles associated with interploid meiosis in order to resolve a chromosomally stable hybrid population. As selection progresses through generations of intermating, there are improvements toward the desired ideotype, but selection efficiency is slowed by apparent antagonisms between annual- and perennial morphological targets and irregular meiosis which is especially problematic in a trigenomic tetraploid. This shows that keys toward perennial crop development through interspecific hybridization will be to capitalize on the abundant phenotypic variation within our population, achieve meiotic stability in order to maximize selection efficiency, and to break genetic correlations between annual and perennial traits.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.556

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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