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Record W2795883455

Hierarchical structure of field pea starches and their impact on physicochemical properties

2016· dissertation· en· W2795883455 on OpenAlexfundaboutno aff
Rakesh Raghunathan

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2016
Typedissertation
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaMemorial University of NewfoundlandSaskatchewan Pulse Growers
KeywordsAmylopectinAmyloseCrystallinityBranching (polymer chemistry)StarchField peaCrystallizationChemistryResistant starchFood scienceCultivarCrystallographyHorticultureOrganic chemistryBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to determine the molecular structure and properties of newly released cultivars of field peas [CDC Golden (CDCG), Abarth (ABAR), CDC Patrick (CDCP) and CDC Amarillo (CDCA)] grown at different locations in Saskatchewan, Canada. Starch yield (on a whole seed basis), apparent amylose, total lipid and surface area were in the range 34-37%, 38.2-42.6%, 1.07–1.38% and 0.31-0.38 m², respectively. The proportion of short (DP 6-12) amylopectin chains, amylopectin branching density, molecular order, crystallinity, crystalline heterogeneity, gelatinization transition temperatures, pasting temperatures, peak viscosity, extent of acid hydrolysis, and resistant starch content were higher in CDCG and ABAR. However, amylopectin long chains (DP 13-26), average chain length and thermal stability were higher in CDCP and CDCA. The results of this study showed that differences in physicochemical properties among cultivars were mainly influenced by amylopectin chain length distribution, amylopectin branching density and co-crystallization of amylose with amylopectin.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.265
Teacher spread0.238 · 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 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueMemorial University Research Repository (Memorial University)→Same topicFood composition and properties→French-language works237,207→