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Record W4385192214 · doi:10.5539/jas.v15n7p64

Characterization of White Lupin Seed Coats

2023· article· en· W4385192214 on OpenAlexvenueno aff
Jada Shaw, Harbans L. Bhardwaj

Bibliographic record

VenueJournal of Agricultural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Research and Chemistry
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsLupinusBiologyLegumeCropCoatCotyledonHorticultureAgronomyBotany

Abstract

fetched live from OpenAlex

White lupin (Lupinus albus L.), a winter legume crop with tremendous potential as a food crop, has been evaluated at Virginia State University for several years. This effort has developed several winter-hardy, high-yielding lines, which vary in alkaloid concentration in the seeds. Current study was conducted to characterize various component of lupin seed especially seed coat and cotyledon portion in seeds of five lupin lines (VSU-1, VSU-1X, VSU-5, VSU-10, and VSU-101). Five hundred seeds of each line were separated into seed coats and cotyledons to record relative proportions. Whole seeds, seed coats, and cotyledons were analyzed to determine concentrations of protein, fiber, fat, iron and zinc. Significant differences were observed among five lines for seed coat proportion, which varied from 22.6 to 25.6 percent. Proportions of protein (7.8, 41.6, and 34.3 percent), fiber (44.7, 1.0, and 12.2 percent), fat (1.3, 10.4, and 8.6 percent), and zinc (14.5, 61.9, and 50.8 percent) concentrations varied significantly for seed coats, cotyledons, and whole seed, respectively but not for iron concentration. Results of this study indicate that separation of seed coats from white lupin seed could be used to develop value-added products; to increase nutritional quality of white lupin seeds; and enhance white lupin’s suitability as a plant protein source.

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 score0.744
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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