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Record W2019895519 · doi:10.1108/eum0000000006249

Similarity assessment in a case‐based reasoning framework for building envelope design

2001· article· en· W2019895519 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLogistics Information Management · 2001
Typearticle
Languageen
FieldComputer Science
TopicAI-based Problem Solving and Planning
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnvelope (radar)Building envelopeSimilarity (geometry)Process (computing)Case-based reasoningBuilding designComputer scienceDomain (mathematical analysis)EngineeringSystems engineeringSoftware engineeringArtificial intelligenceArchitectural engineeringMathematicsProgramming language

Abstract

fetched live from OpenAlex

An important part of the knowledge required for designing the envelope of a new building is based on experience. Confronted with a building envelope design problem, a human expert adds to well‐established domain knowledge his/her own experience or the experience of others, to support his/her reasoning process, and to guide him/her in stereotypical situations. Based on that observation, we can conclude that the building envelope design fits well the description associated with the so‐called “weak theory domains”, and is a prime candidate for adopting a case‐based reasoning (CBR) approach. Proposes strategies to encode, organize, and compare prototypical building envelope cases within a CBR framework for selecting the construction alternatives during the preliminary stage of the building envelope design. The methodology presented aims to find the most suitable design alternative for a new building envelope from a library of prototypical building cases.

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.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.508
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.060
GPT teacher head0.328
Teacher spread0.268 · 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