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Record W6990014903

Climate Zone-Based Energy Retrofits in Canada For Current and Future Climate Change Scenarios

2022· dissertation· en· W6990014903 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental Policies and Emissions
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeElectricityPhotovoltaic systemEnergy consumptionCurrent (fluid)Consumption (sociology)Climate change mitigationElectricity generation
DOInot available

Abstract

fetched live from OpenAlex

The research objective was to investigate mitigation measures for reduction of energy use, emissions, and costs, for a residential house in current and future climates in Vancouver and Toronto, representing climate zones 4 and 5, respectively. The mitigation strategies investigated were: increasing vegetation, increasing thermal resistance, decreasing infiltration/exfiltration, adding a cool roof, and adding photovoltaic panels. Energy consumption simulations were conducted using the Vertical City Weather Generator (VCWG) software. Future climate data was obtained form CanRCM4 files under Representative Concentration Pathway (RCP) 4.5 and 8.5 [W m^{-2}] . The largest cost and CO_2e savings in Vancouver and Toronto came from the reduction in infiltration/exfiltration. The economic analysis showed that cost savings for mitigation efforts are dependent on individual costs in each city. Electricity prices in Vancouver were cheap, which made the use of PV systems too expensive. However, in Toronto the high price of electricity made PV systems cost effective.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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 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 routes1
Has abstractyes

Explore more

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