Construction Methods Feasibility Reasoning in an Integrated Environment
Bibliographic record
Abstract
The construction technology landscape is changing at a pace far outstripping the capacities of a single decision-maker to stay current and take advantage of new and more efficient innovations. At the same time, projects are becoming more diverse and complex, underscoring the need to exploit such innovations. Also, construction companies are becoming bigger and geographically distributed. There exists a need to develop an IT infrastructure to help in the capture and transfer of hard-earned lessons on previous projects across the entire enterprise. In this paper, we present an architecture for such a system. It permits construction users to bank knowledge gained from industry sources and previous projects, and readily reuse these in formulating construction strategies for new projects. Emphasis is placed on how a number of modular data sources specifying the product and process aspects of construction projects can be loosely-coupled to allow reasoning about construction methods and the generation of an initial schedule.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".