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Record W2342961474 · doi:10.2527/msasas2016-266

266 Development of precision gestation feeding program using electronic sow feeders and effects on gilt performance

2016· article· en· W2342961474 on OpenAlexaff
R. Quincy Buis, D. Wey, C. F. M. de Lange

Bibliographic record

VenueJournal of Animal Science · 2016
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAnimal scienceLitterGestationBody weightEnergy requirementBiologyPregnancyEndocrinologyMathematicsEcology

Abstract

fetched live from OpenAlex

Computer controlled electronic sow feeders (ESF) allow precision feeding (PF) of individual gestating sows housed in groups. A study was conducted to evaluate PF gestating gilts using the NRC (2012) nutrient requirement model. The NRC (2012) model was adjusted to estimate daily energy requirements of gestating gilts, based on a constant daily lipid deposition target of 105 g/d, observed BW at breeding, assumed litter size of 12.5 and mean birth weight of 1.4 kg. Eighty gilts were assigned at d2–8 post breeding to 1 of 2 dietary treatments, moved into group-housed ESF pens, and remained there until d101–107 of gestation. For half the gilts (PF), the feeding level and blend of 2 iso-caloric diets (NE 2518Kcal/kg; 0.80 vs. 0.20% SID Lys for high and low protein, respectively; diets HP and LP) were adjusted daily for each animal to accurately meet estimated energy and Lys requirements. The remaining gilts (CON) received constant amounts of feed throughout gestation: 1.32 and 0.88 kg/d of HP and LP diets, respectively (mean SID Lys 0.56%). Total feed allowance per sow (d3–105) was similar for both groups (PF vs. CON; 201 vs. 203 kg; P = 0.66), while sows on PF used 6 kg less of the HP diet. Between treatments (PF vs. CON), d3–105 gains of BW (60.9kg vs. 64.7kg, P = 0.18) and back fat (3.7 mm vs. 3.2 mm, P = 0.47) did not differ. Yet when ADG for early (d 5–32), mid (d 33–67) and late (d 68–103) gestation were compared, gilts on PF tended to gain less in early gestation (0.31 vs. 0.41 kg/d; P = 0.096), while ADG was similar during mid (0.71 vs. 0.73 kg/d; P = 0.704) and higher for PF during late (0.82 vs. 0.66 kg/d; P < 0.01) gestation. During the subsequent 21d lactation period, no treatment effects on performance were observed (litter size at birth 12.2 vs. 12.2; mean birth BW 1.52 vs. 1.47 kg/pig; litter growth rate 2.47 vs. 2.47 kg/d); voluntary ADFI was higher for PF (4.98 vs. 4.56 kg/d; P = 0.045) and ADG tended to be higher for PF (−0.78 vs. –0.98 kg/d; P = 0.10). In this study, PF gilts did not affect overall gestation BW and back fat gain. However, in PF gilts the pattern of sow BW gain followed more closely the gain of products of conception. Gilts on PF ate more and tended to loose less weight during the subsequent lactation, which may benefit long term reproductive performance.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.360
Teacher spread0.306 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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