Transportation Asset Management: Strategic Workshop for Department of Transportation Executives
Bibliographic record
Abstract
The Transportation Research Board (TRB) Task Force on Accelerating Innovation, partnered with the Joint AASHTO–FHWA–NCHRP International Technology Scanning Program, to conduct a 1-day, executive-level workshop on transportation asset management, December 13, 2006, in the National Academy of Sciences Lecture Room, 2100 C Street NW, Washington, D.C. This forum was limited to 15 senior executives and their asset management program managers. The agenda was developed to maximize dialogue and discussion. The program included the following highlights: International roundtable with speakers from Australia; Alberta, Canada; and the United Kingdom; U.S. roundtable and case studies with speakers from Florida, Michigan, Utah, and Ohio Departments of Transportation (DOTs); Focus on the role of asset management in the growing area of public–private partnerships (PPPs); and Extended dialogue time among senior executives. The program offered opportunities to learn how other states and countries have benefited from asset management: Better quantifying the condition of key assets; Improving financial projections by professionally dealing with shortfall and expectations; Improving system performance even with constant or declining dollars; Improving analyses and strategic investment options; Improving internal decision making; Improving dialogue with legislatures, governors, and citizens; Advancing culture change from expenditures to investments; and Applying asset management to better analyze PPPs.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".