Bibliographic record
Abstract
This paper presents a study conducted in collaboration with large Canadian engineering, procurement and construction management (EPCM) firm to identify areas of improvement in the current process of progress reporting and forecasting project status at different targeted future dates.The study focused mainly on trending and time/Cost control of engineering, procurement and construction (EPC) projects.It encompassed a field study of the practices of the industrial collaborator, study of related materials from the literature, and development of standalone computer applications, which serves as add-on utilities to the propriety project management software of the industrial partner.The paper presents a model for improving trending and forecasting of time and cost in construction operations.The proposed model has 3 main functions: 1) trending of estimate accuracy, 2) integrated control and forecasting, and 3) progress visualization.@Risk 5.0 for excel, Windows SharePoint Server and visual basic for application (VBA) are used to develop 3 add-on tools to implement the developments made in the above 3 functions.Numerical examples based on a set of data from a pilot training project, developed by the industrial partner, are presented to illustrate the essential features of the developed model.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".