Value of Bayesian Pre-Posterior Information for Enhancing Life Cycle Analysis of Highway Investments
Bibliographic record
Abstract
Assessing risk in life cycle cost and benefit estimates of highway investments is recommended by major organizations such as the World Bank and the U.S. Federal Highway Administration. This challenging task needs methodological support. Mutually exclusive investment alternatives can differ in terms of costs of construction, maintenance, rehabilitation, and end of life value. Due to many causal factors and the long life of highway infrastructure, these items cannot be estimated with certainty. To go beyond sources of uncertainty study, a method is needed to check economic feasibility of acquiring additional information for deeper insight. This paper reports research on the value of Bayesian pre-posterior information for refining life cycle analysis of uncertain costs and benefits for evaluating highway investment alternatives. Example applications demonstrate how the Bayesian pre-posterior analysis can be applied to check the feasibility of obtaining new information for enhancing life cycle analysis of highway investments. The value of Bayesian pre-posterior information is illustrated for reducing risk. Also, depending upon specifics of uncertain states, a change in the choice of the investment alternative for implementation can be investigated. The product of this research can potentially upgrade highway infrastructure planning and management practice.
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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 | 0.020 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".