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Record W2148490651 · doi:10.1094/cchem-02-15-0029-r

Genetic Gains in Agronomic and Selected End‐Use Quality Traits over a Century of Plant Breeding of Canada Western Red Spring Wheat

2015· article· en· W2148490651 on OpenAlexafffundabout
Pierre Hucl, Connie Briggs, R. J. Graf, Ravindra N. Chibbar

Bibliographic record

VenueCereal Chemistry · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsFarinographCultivarAgronomyYield (engineering)BiologyProductivityPlant breedingHorticultureBotanyAbsorption of water

Abstract

fetched live from OpenAlex

Canadian Western Red Spring (CWRS) market class is the predominant type of wheat ( Triticum aestivum L.) grown in Canada since the turn of the 20th century. Wheat cultivars ranging from cv. Red Fife to cv. Superb were field tested in a series of 24 replicated trials spanning 19 years in central Saskatchewan, Canada. The objective of this study was to measure the rate of cultivar improvement in light of relatively narrow end‐use quality definitions for the CWRS market class. Regression of cultivar trait means on year of cultivar registration was used to assess the rate of change in yield, productivity traits, and end‐use quality parameters. Yield levels were estimated to be increasing at a rate of approximately 15 kg/ha per year in 1970 and 23 kg/ha per year in 1995. Days to spike emergence and plant height decreased over time. Kernel weight, grain protein concentration, SDS sedimentation volume, farinograph absorption, and dough development time increased over time, whereas farinograph mixing tolerance index and yellow pigment concentration decreased. The results show that improvement in key agronomic and end‐use traits has been achieved in CWRS wheat.

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 score0.685
Threshold uncertainty score0.907

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 head0.034
GPT teacher head0.221
Teacher spread0.187 · 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

Citations27
Published2015
Admission routes3
Has abstractyes

Explore more

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