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Record W7133440307

Effect of rumen modifier management on feedlot performance and carcass attributes of steers

2021· other· en· W7133440307 on OpenAlexaboutno aff
A J Nortrup, R S Hegarty, F C Cowley

Bibliographic record

VenueRUNE (Research UNE) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFeedlotMonensinLasalocidRumenAnimal productionFeed conversion ratioProductivity
DOInot available

Abstract

fetched live from OpenAlex

Antibiotic rumen modifiers (ARMs) have been routinely included in feedlot rations to improve rumen function and nitrogen retention (Elsasser 1984), but, with the exception of laidlomycin, no new ARMs are being registered. Alternative management strategies for existing ARMs in feedlot finisher rations have been studied to improve productivity and efficiency. Advantages in average daily gain (ADG) and gain to feed of 4.8% and 2.7%, respectively, have been found in Canada with the daily rotation of monensin and lasalocid compared to monensin alone in steers on finisher rations (Shreck et al. 2016). However, few studies have examined the effect of ARM strategies from feedlot arrival to exit, or compared individual ARM with multiple ARMs in daily rotation over the full feeding period. This study sought to quantify how changes in ARM management might deliver growth, and carcass advantages to the Australian feedlot industry.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.055
GPT teacher head0.366
Teacher spread0.311 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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