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Record W3118671466

The Financial Impact of Unmanned Aerial Vehicles on Construction Project Management

2017· article· en· W3118671466 on OpenAlex
Dominic Aello, Miguel Rodríguez García, Shahab Moeini

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

VenueURSCA Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsSystems engineeringEngineeringProject managementConstruction managementConstruction engineeringEngineering managementRisk analysis (engineering)Process managementComputer scienceBusinessCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

Unmanned Aerial Vehicle (UAV) systems represent an emerging disruptive technology that has gained attention from progressive construction companies. UAV systems as a data acquisition platform and measurement instrument are becoming attractive for many Construction Project Management (CPM) applications. The application of UAV systems for project layout, progress reporting, building inspection and health and safety control has made this technology a critical tool for advancing Building Information Modeling (BIM) in construction projects. Despite the potential to reduce costs while maintaining or improving quality, there is currently resistance toward the integration of the UAV systems as a CPM tool into traditionally managed construction projects. This paper will focus on quantitative and qualitative analysis of collected data regarding the performance evaluation and financial impact of UAV systems on construction projects as an advanced CPM tool. * Indicates faculty mentor

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.305

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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