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Record W2133983145 · doi:10.5539/jas.v4n1p58

Yield and Nutritional Quality Traits of White Lupin Sprouts

2011· article· en· W2133983145 on OpenAlexvenueno aff
Harbans L. Bhardwaj, Anwar A. Hamama

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsLupinusBiologyCropAgronomyYield (engineering)HorticultureWhite (mutation)

Abstract

fetched live from OpenAlex

Use of mature lupin seeds for human nutrition has been prevalent since ancient times. Lupin seed have been used as food for over 3000 years around the Mediterranean and for as much as 6000 years in the Andean highlands. However, no information is available about value-added products from lupin, especially white lupin (Lupinus albus L.), for human consumption. We prepared sprouts from seed of eight white lupin genotypes, grown at two locations in Virginia during 2003-04 crop season. These sprouts were analyzed for various traits. Location effects were significantly for all traits except for moisture content of sprouts whereas genotypic effects were significant only for fresh sprout yield. The mean values for fresh yield (g) and contents (percent on dry weight basis) of moisture, crude fiber, oil, and protein, in white lupin sprouts were 74.8, 78.4, 16.7, 7.6, and 41.3, respectively. Oil and protein contents of white lupin sprouts were similar to alfalfa sprouts whereas white lupin sprouts had higher contents of oil and protein than mungbean sprouts. Based on crude fiber content, white lupin sprouts were superior to alfalfa and mungbean sprouts. Results indicated that white lupin sprouts have potential as human food.

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 score0.888
Threshold uncertainty score0.313

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 head0.069
GPT teacher head0.274
Teacher spread0.205 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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