Sources of Information in Highways: A Bibliography
Bibliographic record
Abstract
This bibliography, containing over 650 entries, is intended to serve as a guide to the major sources of information on highways. While sources listed focus primarily on the United States and Canada, some international materials have been included. Though emphasis is on current publications, some materials of historical interest have also been included. Resources listed in the bibliography include both print and electronic materials, with many Internet sites falling within that latter category. The bibliography was a collaborative effort and was compiled by twelve members of the Transportation Division of the Special Libraries Association. The resultant bibliography is part of a larger Transportation Division project, a revised edition of the Division's multi-volume work, Sources of Information in Transportation. The full, multi-volume new edition (its 5th ) will be available in electronic format on the Internet, with a publication date of late 2001 anticipated. Information concerning the new edition of Sources of Information in Transportation may be found at the Division's website: http://www.library.nwu.edu/transportation/slatran/
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.080 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.056 |
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".