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Record W2086941578 · doi:10.1002/jsfa.4552

Effect of genotype and environment on the concentrations of starch and protein in, and the physicochemical properties of starch from, field pea and fababean

2011· article· en· W2086941578 on OpenAlexaff
Shannon Hood‐Niefer, Thomas D. Warkentin, Ravindra N. Chibbar, Albert Vandenberg, Robert T. Tyler

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2011
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStarchAmyloseField peaChemistryAmylopectinFood scienceBiochemistryPisum

Abstract

fetched live from OpenAlex

BACKGROUND: The effects of genotype and environment and their interaction on the concentrations of starch and protein in, and the amylose content and thermal and pasting properties of starch from, pea and fababean are not well known. RESULTS: Differences due to genotype were observed in the concentrations of starch and protein in pea and fababean, in the onset temperature (To) and peak temperature (Tp) of gelatinization of fababean starch, and in the pasting, trough, cooling and final viscosities of pea starch and fababean starch. Significant two-way interactions (location × genotype) were observed for the concentration of starch in fababean and the amylose content, To, endothermic enthalpy of gelatinization (ΔH) and trough viscosity of fababean starch. Significant three-way interactions (location × year × genotype) were observed for the concentration of starch in pea and the pasting, trough, cooling and final viscosities of pea starch. CONCLUSION: Differences observed in the concentrations of starch and protein in pea and fababean were sufficient to be of practical significance to end-users, but the relatively small differences in amylose content and physicochemical properties of starch from pea and fababean were not.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.201
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 source (direct Gemma or distilled Codex), 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

Citations92
Published2011
Admission routes1
Has abstractyes

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