Bibliographic record
Abstract
This article reports on results of a seven-country study of new road construction management methods funded in large part by the National Technology Agency of Finland, the Finnish Road Administration, and the Finnish Road Enterprise. The Innovative Project Delivery Methods for Infrastructure study reports on developments in Australia, Canada, England, Finland, New Zealand, Sweden and the U.S. The four road construction methods with most promise are Design-build, Design- Build-Operate-Maintain, Design-Build-finance-Operate, and Full Deliver (also known as Project Management). Still, the traditional Design- Bid-Build method is most often used, except in England, which has switched almost entirely to the innovative methods. It shows the different effects on time-lines, budgets; the types of projects to which each is most suited; advantages and disadvantages of traditional and new methods, as described by the agencies using them. Longer term contracts give the contractor the greatest ability to take advantage of new developments in intelligent transportation tools. Using outcomes as criteria for performance also give contractors more freedom to take advantage of alternative methods and innovations. Australia and New Zealand have used 10-year contracts most extensively and have good models. The Design-Build Selector developed by the University of Colorado, Georgia Tech and the National Science Foundation is also a useful tool for choosing contract methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".