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Record W4388181436 · doi:10.1093/jas/skad341.049

55 The Effects of Dietary Net Energy Levels on Grow-Finish Performance and Carcass Characteristics of Market Gilts Managed with Improvest

2023· article· en· W4388181436 on OpenAlexaboutno aff
Yifei Wang, Blaine Hansen, Steve Pollmann, J.L. Landero, Malachy Young, B. M. Bohrer

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal sciencePopulationBiologyMathematicsMedicine

Abstract

fetched live from OpenAlex

Abstract Improvest is a gonadotropin releasing factor (GnRF) analogue-diphtheria toxoid conjugate injection approved for temporary suppression of ovarian function and estrus in market gilts (Zoetis Canada Inc.). Study objectives were to determine the effects of three different net energy levels (LOW – 2.216 Mcal/kg, MID – 2.341 Mcal/kg, or HIGH – 2.466 Mcal/kg) during the grow-finish period on live performance and carcass characteristics of market gilts managed with Improvest (IMP) compared with market gilts not managed with Improvest (CON). The study consisted of 1,008 market gilts (average starting weight of 30.8 kg) in 48 pens (21 pigs/pen) with experimental treatments arranged as a 2 × 3 factorial design with main effects of Improvest (IMP or CON) and net energy level (LOW, MID, or HIGH). An equal number of pigs were marketed from each of the six treatment groups on day-83 (21.9% of the population), day-90 (21.9% of the population), day-97 (21.9% of the population), and day-104 (34.3% of the population) of the experiment. The weighted average for time post-second Improvest injection to marketing was 39.8 days. Data were analyzed with PROC MIXED of SAS, with pen serving as the experimental unit, fixed effects of Improvest (IMP or CON), net energy level (LOW, MID, or HIGH) and their interaction, and a random effect of pen location. There were no significant interactions (P ≥ 0.20) for average daily feed intake (ADFI), average daily gain (ADG), Feed:Gain (F:G), hot carcass weight (HCW), dressing percentage, backfat thickness, or predicted lean yield. However, IMP gilts consumed more feed (6.8% greater ADFI; P < 0.01), grew faster (5.0% greater ADG; P < 0.01), were less efficient (1.5% greater F:G; P < 0.01), were heavier (3.5 kg HCW; P < 0.01), and were fatter (1.9 mm greater backfat thickness and 0.9% less predicted lean yield; P < 0.01) than CON gilts. No difference (P = 0.21) in carcass dressing percentage between IMP and CON gilts was detected (Table 1). There were significant effects (P < 0.05) of net energy level for ADFI, ADG, F:G, HCW, and dressing percentage. Pigs fed LOW diets had the greatest ADFI, slightly less ADG, the least efficient F:G ratio, the lightest HCW, and reduced dressing percentage compared with HIGH (P < 0.01). There were no differences (P > 0.05) for backfat thickness or predicted lean yield among dietary energy level treatments. Overall, these data indicate that typical Improvest response levels were sustained at each of the net energy levels evaluated in this study (response levels for HCW of 2.6 kg for LOW, 3.4 kg for MID, and 4.5 kg for HIGH); however, consideration should still be provided to the known production impacts of low net energy diets when market gilts are managed with Improvest.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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
Published2023
Admission routes1
Has abstractyes

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