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Record W2783864532 · doi:10.22256/pubvet.v12n2a28.1-9

Potencial da ensilagem de capim-braquiaria com inclusão de farelo de arroz: Revisão

2018· article· en· W2783864532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubVet · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRural Development and Agriculture
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsSilageForageFermentationNutrientEffluentBiologyAgronomyLivestockEnvironmental scienceAgricultural scienceMathematicsBiotechnologyFood scienceEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Brazil is one of the countries with the highest potential for livestock production, mainly determined by its climatic conditions, vast territory and forage, that are the basis of the diet of ruminants in most production systems in the country. This assumption, an alternative to solve the lack of food during the year is the conservation of surplus grass as silage. However, the high moisture content at the ensilage predispose the growth of undesirable microorganisms, which result in loss of gases and effluents. However, one of the ways to reduce these losses is the addition of coproduct with high hygroscopic and increase the nutritional value and benefits during the fermentation process of preserving forage. Developed this study aiming to evaluate the fermentation characteristics, gas losses and effluent, nutrient recovery, nutritive value, the in situ degradability and fractionation of carbohydrates and protein in silages added hygroscopic additive, as rice meal.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

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)0.0030.001

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 head0.006
GPT teacher head0.210
Teacher spread0.203 · 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