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Record W2997216864 · doi:10.22215/etd/2019-13718

The Next-Illimitable: Revitalize the limitations arising from green standards for retrofitting existing retail buildings

2019· dissertation· en· W2997216864 on OpenAlexaff
Nazanin Pourali

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsCarleton University
Fundersnot available
KeywordsRetrofittingGreenhouse gasCertificationGreen buildingArchitectural engineeringEnvironmental economicsClimate changeClimate change mitigationEfficient energy useBusinessEngineeringEnvironmental resource managementEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

One of the current pivotal issues globally that have a massive impact on the forthcoming generation is climate change and its consequences. Carbon dioxide (CO 2 ) plays a delicate role in sustaining the appropriate temperature for the planet. It adds to the temperature and is known as a greenhouse gas (GHG). Buildings are one of the producers of GHGs and can be part of the solution to reduce GHGs through their energy performance improvements.

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)0.004
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.007

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 head0.057
GPT teacher head0.294
Teacher spread0.237 · 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
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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