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Record W4323656789 · doi:10.35172/rvz.2023.v30.1052

BIOMASS SORGHUM SILAGES WITH SUGARCANE

2023· article· en· W4323656789 on OpenAlexaff
D’arc Elly Prates de Oliveira, Caroline Salezzi Bonfá, Marcela Azevedo Magalhães, Flávia de Jesus Ferreira, Gabriel Machado Dallago, R. A. da C. Parrella

Bibliographic record

VenueVeterinária e Zootecnia · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsMcGill University
FundersUniversidade Federal de LavrasUniversidade Federal de Viçosa
KeywordsSilageSorghumDry matterBiomass (ecology)AgronomyNeutral Detergent FiberForageHemicelluloseSweet sorghumFermentationLigninCompletely randomized designOrganic matterBiologyAnimal scienceFood scienceBotany

Abstract

fetched live from OpenAlex

The storage of forage to be offered at different times of the year are viable alternatives for all production systems, and sorghum biomass has been highlighted for this purpose. As preserved forage, it was hypothesized that sugarcane can contribute to the fermentation process. The objective was to evaluate the inclusion of different levels of sugarcane (0, 20, 40 and 60%) in the silage of three biomass sorghum genotypes (B012, B017 and B018). The material was ensiled using PVC silos and after 60 days the silos were opened and the contents of dry matter, mineral matter, organic matter, crude protein, neutral detergent fiber, acid detergent fiber, hemicellulose, lignin, and hydrogen potential were determined. The experiment was conducted in a completely randomized design, in a factorial scheme with four replications. The data were analyzed through the analysis of variance followed by multiple comparison by Tukey's test (α < 0.05) and linear regression. The biomass sorghum genotypes responded satisfactorily to the fermentation process, resulting in quality silages. However, the inclusion of sugarcane did not improve the quality of the silages, and its inclusion in the silage of the genotypes evaluated is not recommended.

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 score0.958
Threshold uncertainty score0.979

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.228
Teacher spread0.201 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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