Bibliographic record
Abstract
This synthesis report will be of interest to state department of transportation (DOT) chief administrative and information officers, information technology staff, project managers, and their supervisors. It describes the current state-of-the-practice for DOT project management systems. This includes information on project management framework and project management knowledge areas. In addition, several case studies and an appendix chapter on change: definition and implementation, role of personnel, processes, and cost are also provided. Information for the synthesis was collected by surveying U.S. and Canadian transportation agencies and by conducting a literature search. This report of the Transportation Research Board describes the process used to develop and implement automated project management systems, the source of software in use, and the extent of any modifications necessary for commercial products to fit DOT business needs, the operating environments for systems in use, and the capabilities and limitations of the systems to track multiple projects and information sources. Information on system capabilities and deficiencies in project communications, report and problem solving, necessary resources required to implement and maintain each automated system, and how long each system has been in place and future plans for long-term enhancements, modifications, or replacements is also included.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.120 |
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".