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Record W2103542225 · doi:10.1061/9780784412329.060

A Semantic Approach to Representation, Sharing, and Discovery of Construction Simulation Models

2012· article· en· W2103542225 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

VenueConstruction Research Congress 2012 · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComposabilityComputer scienceInteroperabilityReuseStructuringMetadataOntologyBuilding information modelingSemantic WebInformation modelData modelingRepresentation (politics)Software engineeringData scienceInformation retrievalWorld Wide WebDistributed computingEngineering

Abstract

fetched live from OpenAlex

Simulation models are knowledge intense computational entities. Simulation model development consumes lots of time and resources. The research described in this paper is motivated by necessity of reuse, composability and interoperability of the construction simulation models. The innovate architecture presented in this paper blends semantic web technologies and construction simulation models presented in a machine interpretable metadata for storing, sharing and discovery of knowledge within construction simulation models and properly linking them to their related information sources. For this purpose a repository of construction simulation models has been developed. Construction modeling repository mainly contains simulation templates and models and other relevant sources. Sharing and integration of these simulation componets enables inference and extraction of meaningful information through structuring queries.

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)0.001
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 score0.779
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.072
GPT teacher head0.334
Teacher spread0.262 · 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