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Record W2103323926 · doi:10.7451/cbe.2014.56.5.1

Energy consumption of heat pads and heat lamps and aerial environment in a commercial swine farrowing facility

2014· article· en· W2103323926 on OpenAlexfundvenueno aff
E. Besheda, Qiang Zhang, Ray Boris

Bibliographic record

VenueCanadian Biosystems Engineering · 2014
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
FundersManitoba HydroDe La Salle University
KeywordsEnvironmental scienceEnergy consumptionHeat energyWaste managementAutomotive engineeringNuclear engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

An experimental study was conducted to compare two localized heating methods, namely heat pads and heat lamps, in a commercial swine farrowing facility. Two farrowing rooms each with 44 crates were instrumented for monitoring room environmental conditions (temperature and relative humidity) and energy consumption. One room was equipped with 175W (per crate) heat lamps and the other room with 65W (per crate) heat pads. The piglet mortality and weight gain were recorded. The air temperature in the two rooms was maintained at the same level using environmental controllers (set at the same setpoint). However, the relative humidity in the lamp room was found to be lower than that in the heat pad room probably due to more ventilation required to remove more sensible heat produced by the lamps. There were no significant differences in the mortality rate and weight gain between heat pads and heat lamps. The daily energy consumption by heat pads was 2.9 kWh less than that by heat lamps per crate. This represents a 73% saving of energy required for localized heating.

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 score0.003
Threshold uncertainty score0.006

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 head0.024
GPT teacher head0.221
Teacher spread0.198 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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