MétaCan
Menu
Back to cohort
Record W4401705597 · doi:10.1002/cche.10823

Evaluating the quality characteristics of intermediate wheatgrass grown in the Canadian prairies

2024· article· en· W4401705597 on OpenAlexaffabout
Mackenzie Ferguson, Anashwar Valsalan, Douglas J. Cattani, Maneka Malalgoda

Bibliographic record

VenueCereal Chemistry · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemistryQuality (philosophy)AgronomyBotanyBiology

Abstract

fetched live from OpenAlex

Abstract Background and Objectives Intermediate wheatgrass (IWG) is an underutilized perennial grain in the Canadian market with excellent nutritional and environmental benefits. The aim of this study was to assess the impact of the growing environment on the physiochemical and functional properties of IWG grown in three different growing locations in the Canadian prairies. IWG flours were compared between growing locations and years as well as alongside Canada Western Red Spring Wheat whole meal (CWRS‐WM) and refined flour. Findings The proximate composition of IWG flours between years and growing locations had significant ( p < .05) differences for protein, fiber, and ash content but showed little difference for starch content. Owing to differences in proximate composition, variances in mixing parameters, starch pasting properties, and protein profiles were observed. Overall IWG performed poorly in a bread system at 100% incorporation largely due to its lack of high molecular weight glutenin. Conclusion Understanding the impacts that growing location and year have on the proximate composition and therefore working parameters will aid in the development of new wheat products fortified with IWG. These products will contribute to a more resilient and diverse food system, that will provide nutrient‐dense food options for consumers. Significance and Novelty There is very limited research on IWG grown in the Canadian prairies, and this study is among the first studies focused on characterizing IWG grown in Canada.

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 score0.640
Threshold uncertainty score0.954

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 head0.048
GPT teacher head0.342
Teacher spread0.293 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCereal ChemistrySame topicTurfgrass Adaptation and ManagementFrench-language works237,207