CTAN for Risk Assessments Using Multilevel Stochastic Networks
Why this work is in the frame
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Bibliographic record
Abstract
Measuring projects’ cost and schedule risks in an integrated framework using simulation has several modeling challenges that have yet to be addressed by researchers. This paper presents a multilevel network modeling approach that aims to integrate a combination of different networks in one framework, and presents a computer simulation implementation to the cost and time risk assessment network (CTAN). The CTAN is an integrated network that includes uncertainties in the realization of the schedule logic, in activities durations, in project scope, and in cost. The simulation model is a decision support simulation system (DSSS) that currently consists of three modules: the CTAN, the stochastic decision trees, and the stochastic shortest/longest rout network. The CTAN-DSSS may be used in cost and schedule risk assessment. It completely integrates with other DSSS networks and deals with risks associated with cost, time, and scope at equal importance. The DSSS was verified by conducting several tests and validated by its extensive use for both undergraduate and graduate courses in Civil Engineering at the University of Calgary over the last three years.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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