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Record W4390114512 · doi:10.1002/9781394264933.ch8

BIM, GIS

2023· other· en· W4390114512 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

Venuenot available
Typeother
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsYork University
Fundersnot available
KeywordsInteroperabilityComputer scienceBuilding information modelingGeographic information systemSmart cityData sharingProcess (computing)AM/FM/GISInformation systemData scienceData model (GIS)DatabaseWorld Wide WebGIS applicationsInternet of ThingsEngineeringGeography

Abstract

fetched live from OpenAlex

An information system is used to store, organize and structure data in digital database management systems, to process and analyze data for the sharing and distribution of information and knowledge. Building information modeling (BIM) is a collaborative process between different professions linked to the lifecycle of a building or linear infrastructure. There are significant synergies between BIM and geographic information systems (GIS) to integrate and work together in many urban infrastructure and building applications. However, interoperability of these two systems is a main challenge due to inconsistent data formats, semantics definitions, and dissimilar data models. The acquisition of data from multiple sensors and Internet of Things (IoT) linked to a building or a territory concerns both GIS and BIM. The IoT information integration with BIM and GIS data is reaching extreme interest for smart city applications such as Digital Twin development, accordingly, evolving toward Smart Data, a machine-readable information.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.115
Threshold uncertainty score0.998

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)0.0030.008

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 head0.215
Teacher spread0.204 · 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

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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