Front End Planning Tool (FEPT) Based on an Electronic Process Management
Bibliographic record
Abstract
Research indicates that good front-end planning (FEP) results in the achievement of higher levels of project performance. By facilitating collaboration among stakeholders in diverse locations with the use of workflow-enabled processes, such pressures can be reduced, and the overall process and results of FEP can be improved. With these goals, a front-end planning tool (FEPT) has been developed as support for owners and major contractors who are engaged in front-end planning. This paper presents the new FEPT and describes how it has been used for construction megaprojects in the nuclear power, oil and gas, and mining industries. The paper begins with the definitions related to and an explanation of the general process for implementing and applying the FEPT and then describes and analyzes how the FEPT was applied in case study projects in order to test its validity. The results indicate that the FEPT increases the efficiency and effectiveness of front-end planning for the megaprojects studied and that it has the potential to produce similar results for other megaprojects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".