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Record W3149211648 · doi:10.4141/cjps2012-331

Effect of maturity on physicochemical and cooking characteristics in yellow peas (<i>Pisum sativum</i>)

2013· article· en· W3149211648 on OpenAlexaffvenueabout
N. Wang, G. Castonguay

Bibliographic record

VenueCanadian Journal of Plant Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPisumSativumStachyoseHorticultureStarchChemistryPhytic acidRaffinoseAnimal scienceFood scienceBiologyBotanySucrose

Abstract

fetched live from OpenAlex

Wang, N. and Castonguay, G. 2014. Effect of maturity on physicochemical and cooking characteristics in yellow peas (Pisum sativum). Can. J. Plant Sci. 94: 565–571. The effect of maturity on physicochemical and cooking properties of yellow peas was investigated. Results indicated that when compared with mature yellow peas, immature yellow peas exhibited significantly lower seed weight (16.4–18.1% less) and smaller seed size (5.9–6.9% less), but higher water hydration capacity (3.7–11.5% more). Significant shorter cooking time but higher firmness value of cooked seeds was observed for immature yellow peas than for mature yellow peas. Immature yellow peas contained significantly higher mean protein content (254.2 vs. 234.6 g kg −1 DM), higher mean crude fiber (67.8 vs. 63.0 g kg −1 DM), and higher mean ash content (31.5 vs. 29.4 g kg −1 DM), but significantly lower mean starch content (432.5 vs. 451.3 g kg −1 DM) as compared with mature yellow peas. Sucrose content was significantly higher in immature yellow peas than that in mature yellow peas, whereas stachyose and verbascose contents were significantly higher in mature yellow peas than in immature yellow peas. Phytic acid content in immature yellow peas was significantly higher than that in mature yellow peas, while trypsin inhibitor activity was not significant. This information will be useful in setting the grading standards for yellow peas 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)0.000
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 score0.847
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations13
Published2013
Admission routes3
Has abstractyes

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