Bibliographic record
Abstract
One main shortcoming in the use of life-cycle cost analysis (LCCA) for analyzing long-term infrastructure projects is the uncertainty in the value of the LCCA parameters. Probabilistic LCCA incorporates these elements of uncertainty by assigning probabilistic values to cost and performance parameters. Studies that have performed probabilistic LCCA in the infrastructure domain propose a probability-based framework for alternative comparison. Although such frameworks convey a wealth of probabilistic information, they are not well suited to decision making. This study proposes a risk-based framework that is similar to techniques used in portfolio risk management. To illustrate the use of such a framework, a Monte Carlo simulation is used to perform probabilistic LCCA for a highway project. Two highway investment opportunities with varying risks and returns are analyzed. The decision framework is used to compare the simulation results with some common investment opportunities in the market. This framework enables private-sector investors to assess the relative risks and returns of alternative infrastructure projects. The fact that similar frameworks are used in the financial investment domain makes this approach suitable for the economic analysis of privatized infrastructure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".