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Record W2970101049 · doi:10.1093/jas/sky073.168

171 Effect of Backgrounding and Feedlot System Strategies on May-Born Steer Performance.

2018· article· en· W2970101049 on OpenAlexaboutno aff
McKay Erickson

Bibliographic record

VenueJournal of Animal Science · 2018
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsFeedlotComputer scienceEconometricsBusinessMathematicsAnimal scienceBiology

Abstract

fetched live from OpenAlex

A 6-yr study examined the effects of differing backgrounding and feedlot systems on May-born steer performance was conducted at Gudmundsen Sandhills Laboratory (GSL), Whitman, NE, and West Central Research and Extension Center (WCREC), North Platte, NE. Weaned steers (n = 392) were blocked by BW and randomly assigned to one of two backgrounding treatments: meadow hay ad libitum and 1.81 kg/d of a 33% CP (DM) supplement (HI) or allowed to graze dormant sub-irrigated meadow with 0.45 kg/d supplement (LO). Steers were placed on backgrounding treatments for 136 d from January to May. In May, one-half of the steers from each backgrounding treatment were placed in the WCREC feedlot system (CALF). The remaining steers grazed upland range at GSL and were transported to the WCREC feedlot mid-September. In yr 2 – 5, steers were fed in a GrowSafe (GrowSafe Systems Ltd., Airdrie, AB, Canada) feeding system. Over the backgrounding period, HI steers had a greater (P < 0.01) ADG (0.64 vs. 0.35 ± 0.03 kg/d, HI vs. LO) and May BW (275 vs 244 ± 2 kg, HI vs. LO). Feedlot entry BW differed (P = 0.02) by combination of development and feedlot system, with HI-YRL steers having the greatest BW (353 ± 3 kg), followed by LO-YRL (341 ± 3 kg), HI-CALF (264 ± 3 kg), and LO-CALF (237 ± 3 kg). Gain:Feed ratios were improved (P = 0.01) in LO steers (0.16 vs. 0.15 ± 0.002 kg:kg, LO vs. HI). At slaughter, HCW was greater (P < 0.01) for HI development (419 vs. 407 ± 3 kg, HI vs. LO) and YRL feedlot system (426 vs. 401 ± 3 kg, YRL vs. CALF). Yield grade was greater (P = 0.04) for the YRL system (3.3 vs. 3.2 ± 0.05, YRL vs. CALF). Marbling score tended to be greater (P = 0.10) for LO development (480 vs. 467 ± 6; LO vs. HI) and was greater (P < 0.01) for YRL system (490 vs. 457 ± 6, YRL vs. CALF). Alternative backgrounding and feedlot systems impacted steer feedlot and carcass traits.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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
Published2018
Admission routes1
Has abstractyes

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