Bibliographic record
Abstract
The concept of cold mix processes has been around for many decades and many different forms have come and gone as the cold technology evolved. In recent years the cold mix technology has made significant improvements in quality and performance. These improvements especially in the chemicals utilized in the production of the asphalt emulsions have allowed for more innovative uses of the cold mix technology. The use of cold mix processes has expanded the toolbox available to agencies to solve their problems. The tightening of highway budgets has altered the way agencies handle construction and rehabilitation. For instance the cold in place recycling technology has become a standard rehabilitaion process used by highway departments and county agencies across the country. The use of cold processes can help in filling a void in reconstruction/rehabilitation. Cold processes can help to lower the greenhouse gases in the atmosphere as they use less energy, as well as cut down on the use of non renewable resources such as aggregates and oil based products. Cold mix processes can help to meet the requirements of the Kyoto Accord and still provide a technologically sound solution to highway agency probelms. This paper presents an overview of the various cold mix processes being utilized across the country and state to which these processses have been elevated as cold technology has expanded and improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".