Collaborative work model under distributed construction environments
Bibliographic record
Abstract
Efficient management of construction project information is essential for the successful performance of a construction project. This paper investigates a standard information classification system, which represents a data model relating 3D CAD drawing, costing, scheduling, resource information, specification, and other information for multiple uses in the project. In sharing information among independent applications in distributed construction environments, a distributed transaction service module that manages information-sharing processes through an integrated database is also desirable. To examine a distributed database structure and data transaction services, this paper analyzes a standard information classification system and business process. A distributed database structure model and a data transaction service module for information sharing are designed. A prototype system is implemented and then applied to a real project for its validation. The target application area of the prototype is a high-rise steel-structure apartment building. The scope of the application includes information on the architectural and structural design, the cost, the schedule, the resources, the quality of the management, and the specifications.Key words: CIC, PMIS, 4D CAD, construction management, construction information, information classification system, integrated management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".