MétaCan
Menu
Back to cohort
Record W2899219470 · doi:10.1139/cjas-2018-0031

Nutritional recommendations and management practices adopted by feedlot cattle nutritionists: the 2016 Brazilian survey

2018· article· en· W2899219470 on OpenAlexvenueno aff
A. C. J. Pinto, D. D. Millen

Bibliographic record

VenueCanadian Journal of Animal Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsFeedlotSilageDry matterAnimal scienceNeutral Detergent FiberBiotechnologyAgricultural scienceBiology

Abstract

fetched live from OpenAlex

The feedlot industry in Brazil is still evolving, and some nutritional management recommendations adopted by nutritionists changes from year to year. The main objective of this survey was to provide a snapshot of current nutritional management practices adopted in Brazilian feedlots. The 33 nutritionists surveyed were responsible for approximately 4 228 254 animals. Corn remained as the primary source of grain used in feedlot diets by the participants, whereas fine grinding was the primary grain processing method. Corn silage was the primary roughage source indicated by nutritionists, and for the first time, physically effective neutral detergent fiber was the preferred fiber analysis method. The average dietary fat recommended was 50 g kg −1 of dry matter, which is about 10% higher than values reported in previous surveys. The use of truck-mounted mixers increased, which may have increased the percentage of feedlots using programmed feed delivery per pen, allowing the increase of energy content of finishing diets. Feedlots did not increase their capacity and nutritionists reported an improvement in feeding management. Results reported in the current study provide a baseline that can be used to improve practices and aid in the development of feedlot industry in Brazil and similar tropical climates.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.042
GPT teacher head0.283
Teacher spread0.241 · 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 designObservational
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

Citations100
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Animal ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207