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Record W2102270393 · doi:10.1108/09699980310478412

Photo‐net: an integrated system for controlling construction progress

2003· article· en· W2102270393 on OpenAlexaff
Jorge Abeid, David Arditi

Bibliographic record

VenueEngineering Construction & Architectural Management · 2003
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsNetwork for Business Sustainability
Fundersnot available
KeywordsComputer scienceCritical path methodAnimationScheduling (production processes)Path (computing)Real-time computingSystems engineeringOperating systemComputer graphics (images)Engineering

Abstract

fetched live from OpenAlex

A scheduling and progress control system called Photo‐net is introduced where a digital movie of construction activities can be played back along with an animation of as‐built vs. as‐planned performance of these activities. A technique to make time‐lapse digital films of construction activities is used. A method to store a digital film is developed allowing thousands of pictures to be stored and managed in a Windows environment. A program is developed that generates critical path method (CPM) derived bar‐charts. A recording system is devised enabling the user to specify the day‐by‐day progress achieved in construction activities, allowing the program to link the playback film with the progress observed on the construction site. A set of input screens are generated by the system that provide a friendly, intuitive and easy way to enter project data. A case study is presented where the system is used, the performance of the system is discussed and the results are analyzed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.005

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2003
Admission routes1
Has abstractyes

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