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Record W2804999232 · doi:10.1139/cjas-2018-0018

Comparative effects of grain source on digestion characteristics of finishing diets for feedlot cattle: steam-flaked corn, barley, wheat, and oats

2018· article· en· W2804999232 on OpenAlexvenueno aff
A. Plascencia, V. M. González-Vizcarra, R. A. Zinn

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsDigestion (alchemy)Dry matterFeedlotRumenChemistryStarchAgronomyOrganic matterAnimal scienceBiologyFood scienceFermentationChromatography

Abstract

fetched live from OpenAlex

Twelve Holstein steers (454 ± 32 kg) with cannulas in rumen and proximal duodenum were used in a generalized randomized block design to compare steam-flaked corn (SFC), barley (SFB), wheat (SFW), and oats (SFO) as grain sources (74% of diet dry matter, flake density = 0.31 kg L−1) on characteristics of digestion. Ruminal organic matter (OM) digestion was similar for SFC-, SFB-, and SFW-based diets, but lower for SFO. Ruminal microbial efficiency was greater for SFW and SFO. Given that the undegradable intake protein (UIP) value of SFC is 57%, the comparative UIP values for SFB, SFW, and SFO were 39.8%, 36.1%, and 47.3%, respectively. Ruminal starch digestion was lower for SFC than the other flaked grains. Total-tract OM digestion and digestible energy were greatest for SFC, intermediate for SFB and SFW, and lowest for SFO. Given the net energy maintenance (NEm) value of SFC is 2.40 Mcal kg−1, the comparative NEm value for SFB, SFW, and SFO were 2.25, 2.36, and 2.17 Mcal kg−1, respectively. Steam flaking results in important modifications of small grains that appreciably affect their comparative feeding value. Current standards underestimates the NE value of SFB, SFW, and SFO, and overestimates the UIP values for SFB and SFO.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.256
Teacher spread0.227 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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