Bibliographic record
Abstract
Investing in winter transportation operations is essential and beneficial to the public and the economy. The U.S. economy cannot afford the cost of shutting down highways, airports, etc., during winter weather. In the northern U.S. and other cold-climate areas, winter maintenance operations are essential to ensure the safety, mobility, and productivity of transportation systems. Agencies are continually challenged to provide a high level of service and improve safety and mobility in a fiscally and environmentally responsible manner. To this end, it is desirable to use the most recent advances in the application of materials, practices, equipment, and other technologies. Such best practices are expected to improve the effectiveness and efficiency of winter operations, to optimize material usage, and to reduce associated annual spending, corrosion, and environmental impacts. Currently, no professional societies, scientific journals, or textbooks are dedicated solely to sustainable winter road operations, and key information is scattered across a variety of disciplines. The objective of the proposed book is to summarize the best practices and recent advances in sustainable winter road operations for the purposes of education and workforce development. This book is now in press and can be cited as follows: Shi, X., Fu, L. (2017). Sustainable Winter Road Operations (Eds.). ISBN: 978-1-119-18506-2. Wiley-Blackwell.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.025 |
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".