MétaCan
Menu
Back to cohort
Record W4224212412 · doi:10.1139/cjas-2021-0111

The effects of grinding and pelleting on nutrient composition of Canadian pulses

2022· article· en· W4224212412 on OpenAlexafffundvenueabout
Cara Cargo-Froom, Rex W. Newkirk, Christopher P. F. Marinangeli, Anna K. Shoveller, Yongfeng Ai, Elijah G. Kiarie, Daniel A Columbus

Bibliographic record

VenueCanadian Journal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of SaskatchewanUniversity of GuelphGenome Prairie
FundersMitacsSaskatchewan Pulse GrowersSwine Innovation Porc
KeywordsMethionineFood scienceComposition (language)LysineChemistryNutrientTyrosineHistidineMealAmino acidSoybean mealIngredientAnimal scienceBiologyBiochemistryRaw material

Abstract

fetched live from OpenAlex

Understanding the effects of processing pulses is required for their effective incorporation into livestock feed. To determine the impact of processing, Canadian peas, lentils, chickpeas, and faba beans, plus soybean meal (SBM; as a comparison), were ground into fine and coarse products and pelleted at three different temperatures (60–65, 70–75, and 80–85 °C). Grinding increased crude protein content in all the pulses ( P < 0.05), but did not affect most amino acids (AA) ( P > 0.05). Pelleting increased crude protein content in Amarillo peas, Dun peas, and lentils ( P < 0.05), but decreased in SBM ( P < 0.05). Pelleting increased cysteine, lysine (Lys), and methionine, and decreased histidine and tyrosine (Tyr) in most pulses ( P < 0.05). Comparatively, pelleting significantly increased Lys and decreased Tyr content in SBM ( P < 0.05). These results suggest that processing can positively affect protein and AA content of pulses. However, specific effects on nutritional composition differed across ingredient type.

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 score0.915
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.013
GPT teacher head0.202
Teacher spread0.190 · 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

Citations10
Published2022
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicPhytase and its ApplicationsFrench-language works237,207