Modeling Scheduling Uncertainty in Capital Construction Projects
Why this work is in the frame
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Bibliographic record
Abstract
Capital infrastructure projects with long-term implementation time frames are generally uncertain in nature. Engineers and planners attempting to estimate the costs of such projects often resort to using contingencies based on their experience without proper modeling of the uncertainty of costs, durations, or economic conditions. This paper presents a simulation-based model for assessing uncertainty associated with these projects. In particular the model accounts for expected fluctuations in the costs and durations of various work packages and, most significantly, it accounts for the inflation of costs over time based on when the work packages occur. The model uses Monte Carlo simulation techniques to account for time and cost and uses non-stationary time series modeling techniques to predict inflation rates. The model is implemented as a special purpose simulation template available in the public domain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it