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Record W4400989880 · doi:10.69554/ajyh5686

Understanding ORAT and how ADM's ORAT programme contributes to sustainability

2022· article· en· W4400989880 on OpenAlexaboutno aff
Sarah Talbot

Bibliographic record

VenueJournal of airport management · 2022
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityDeconstruction (building)Greenhouse gasProject commissioningEnvironmental economicsProcess (computing)BusinessLife-cycle assessmentOperations managementEngineeringWaste managementEconomicsComputer sciencePublishingProduction (economics)Political science

Abstract

fetched live from OpenAlex

This paper discusses how operational efficiency and sustainability are two preoccupations for airport administrations. ORAT's objective is to operate a new facility on day one as if it had been operated for years. By engaging stakeholders in the process, Aéroports de Montréal's (ADM's) Operational Readiness and Airport Transition (ORAT) programme contributes to life cycle analysis, decreases the risk of changes during construction and lowers the post-opening modifications, which results in less deconstruction and material loss. It supports commissioning in the attempt to achieve a cost-effective strategy for reducing energy, costs and greenhouse gas (GHG) emissions in buildings. In short, ADM's ORAT programme promotes sustainability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.324

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 head0.027
GPT teacher head0.216
Teacher spread0.189 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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