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Record W4293526666 · doi:10.3390/ani12172227

Supplementation of Molasses-Based Liquid Feed for Cattle Fed on Limpograss Hay

2022· article· en· W4293526666 on OpenAlexaff
Daciele S. Abreu, José Carlos Batista Dubeux, Luana Dantas Queiroz, David M. Jaramillo, Erick R. S. Santos, Flávia de Oliveira Scarpino van Cleef, Carlos García, Nicolás DiLorenzo, Martin Ruiz‐Moreno

Bibliographic record

VenueAnimals · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHayAnimal scienceForageAlfalfa hayFood scienceAgronomyBiologyRumenFermentation

Abstract

fetched live from OpenAlex

Two experiments were performed to evaluate the effects of (1) different levels of liquid supplementation (LS) based on molasses enriched with 32% (as fed; 45% on DM basis) crude protein (CP) on intake of Hemarthria altissima hay (LH), digestibility, and rumen fermentation, and (2) different levels of LS based on molasses enriched with 32% CP in the in vitro gas production in LH diets. In Exp. 1, twelve heifers and 12 adult male castrated and cannulated cattle were used. Treatments were allocated in a randomized block design, in four treatments: (CTL) access ad libitum to the LH; (SUP2) ad libitum access to LH and supplementation with 0.9 kg d−1; (SUP4) ad libitum access to LH and supplementation with 1.8 kg d−1 and; (SUP6) ad libitum access to LH with 2.7 kg d−1. In Exp. 2, treatments were carried out in a randomized block design with four different proportions of LH diet: (CTL) 100 LH, (SUP2) 85 LH and 15 LS, (SUP4) 70 LH and 30 LS, (SUP6) 55 LH and 45% LS. In Exp. 1, liquid molasses-based supplementation did not affect LH intake (p > 0.05). Molasses intake improved as the supply increased, not reducing the intake of LH. In Exp. 2, the addition of LS to the LH caused a change in the VFA profile, with an increase in propionate production in vitro.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.837

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)0.0010.000

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 head0.038
GPT teacher head0.283
Teacher spread0.244 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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