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Record W4410418289 · doi:10.1002/csc2.70085

Breeding potential of cultivated lentil for increased protein and amino acid concentrations in the Northern Great Plains

2025· article· en· W4410418289 on OpenAlexafffundabout
Derek Wright, Jiayi Hang, James D. House, Kirstin E. Bett

Bibliographic record

VenueCrop Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsUniversity of ManitobaUniversity of Saskatchewan
FundersSaskatchewan Pulse GrowersWestern Grains Research FoundationGenome CanadaBASF
KeywordsBiologyAgronomyAmino acidBiotechnologyBotanyBiochemistry

Abstract

fetched live from OpenAlex

Abstract The rising demand for plant‐based proteins has intensified interest in pulse crops due to their high protein concentration. Few studies have evaluated protein and amino acid composition or variability in cultivated lentil (Lens culinaris Medik.). We evaluated protein and amino acid composition using near‐infrared reflectance spectroscopy in a diversity panel grown in four site‐years in Saskatchewan, Canada, followed by genome‐wide association analyses with phenology‐related traits as covariates. We found little correlation between protein concentration and days from sowing to flowering, region of origin, cotyledon color, or seed size. Reproductive period was correlated with protein concentration, however. We also observed variability among environments and more variability within market classes than among them. We demonstrate the potential for breeders to identify adapted germplasm and select for increased protein and amino acid concentration and quality. We were able to identify several molecular markers for use in marker‐assisted breeding to select for protein concentration or quality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes3
Has abstractyes

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