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Record W4281491416 · doi:10.32920/19775398.v1

Technical, economical, and environmental feasibility of air source heat pump in cold climate for residential houses -Canada

2022· preprint· en· W4281491416 on OpenAlexafffundabout
King Tung

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsHeat pumpGreenhouse gasEnvironmental scienceNatural gasAir source heat pumpsHeating systemStorage heaterCold climateRenewable heatWaste managementEnvironmental engineeringEngineeringMeteorologyHybrid heatMechanical engineeringHeat exchangerGeographyEcology

Abstract

fetched live from OpenAlex

<p>With increasing concern towards global warming and the deadline of the Paris Agreement 2030 coming up, Canada is struggling to meet the desired greenhouse gas emission reductions. As new energy-efficient technology emerges, heating systems in Canada starts to move away from natural gas heating systems to efficient electrical heating systems such as air source heat pump. Though there are many studies related to reducing space heating, there are few studies performed on transitional technologies that are designed to slowly shift from natural gas-dependent society to an electrically powered society. This study analyses a smart switching system for a natural gas and air source heat pump dual system, and a cold climate air source heat pump water heater. These systems can significantly reduce greenhouse gas (GHG) emissions compared to a typical natural gas-fired heater. With these technologies Canada’s residential sector could potentially meet Canada’s Paris Agreement goals.</p> <p><br></p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.226
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2022
Admission routes3
Has abstractyes

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