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Record W1969156154 · doi:10.2134/agronj2013.0295

Evaluation of Wheat Cultivars to Test Indirect Selection for Organic Conditions

2014· article· en· W1969156154 on OpenAlexafffundabout
H. A. Pswarayi, Hiroshi Kubota, Heather E. Estrada, Dean Spaner

Bibliographic record

VenueAgronomy Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsAlberta Ministry of Agriculture and ForestryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCultivarOrganic farmingAgronomyYield (engineering)Selection (genetic algorithm)TraitBiologyPlant breedingEnvironmental scienceAgricultureEcologyMaterials science

Abstract

fetched live from OpenAlex

There is debate regarding direct or indirect selection for organic conditions. Our objective was to evaluate the progress of indirectly selecting organic cultivars in conventional environments. Canadian spring wheat ( Triticum aestivum L.) cultivars, developed for conventional environments from 1885 to 1999 and from 1975 to 2009, respectively, were grown in two separate experiments to assess progress of yield and associated agronomic traits due to breeding. The first experiment evaluated 27 cultivars in organic and conventional conditions for 3 yr (2002, 2003, and 2004), on three sites in western Canada. In the second experiment, eight cultivars were evaluated in organic conditions in 2010 and 2011 at the University of Alberta, Canada. The first experiment showed that breeding had improved yield and most associated traits only in conventional systems and a few associated traits in organic conditions. The second experiment showed that breeding had made significant improvements in yield and test weight in organic conditions. This study suggests that with sufficient quality and disease resistance criteria in place for the breeding of wheat in conventional environments, it may be possible to concomitantly improve wheat yield destined for organic growing conditions. However, fewer associated traits showed significant improvement in organic conditions and improvement rates were lower than in conventional conditions. This suggests that optimizing trait performance in organic conditions should include organic conditions during breeding and selection.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.046
GPT teacher head0.254
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 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

Citations8
Published2014
Admission routes3
Has abstractyes

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