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Record W2061964330 · doi:10.1300/j411v14n01_07

Advances in Breeding of Seed-Quality Traits in Soybean

2005· article· en· W2061964330 on OpenAlexaff
Istvan Rajcan, Guangyun Hou, Aron D. Weir

Bibliographic record

VenueJournal of Crop Improvement · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNutraceuticalBiotechnologyLivestockBiologyCultivarCropIsoflavonesAgronomyFood science

Abstract

fetched live from OpenAlex

SUMMARY Soybean is the leading oil and protein crop of the world, which is used as a source of high quality edible oil, protein, and livestock feed. Soybean seeds also contain carbohydrates, ash and a number of minor compounds with potential nutraceutical properties. For several decades, soybean breeders have focused on improving the oil quality and on increasing the protein content for both feed and human food, such as tofu. Mutation breeding and, more recently, biotechnology, have been used. Newly emerging areas of research and an improved understanding of the genetic control of nutraceutical compounds such as isoflavones, should facilitate the breeding and development of new soybean cultivars with enhanced healthy properties.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.097

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 head0.022
GPT teacher head0.273
Teacher spread0.250 · 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

Citations39
Published2005
Admission routes1
Has abstractyes

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