Bibliographic record
Abstract
This article describes how the use of crumb rubber in an asphalt mix goes back 40 years, particularly in the U.S. Southwest, but the technology is growing in application throughout the world. The biggest factor is the realization that tire rubber can be used as a substitute to virgin polymers commonly used in asphalt. The switch from a virgin polymer to a tire rubber component is a driving factor for increased usage, and of course that is related to the cost of crude oil. As crude oil has increased, the cost of polymers has increased. Cost savings is the biggest issue in today’s market, with high asphalt prices and high polymer prices. Tire rubber’s been steady in price – very stable over the past 15 years. If state agencies are not using tire rubber routinely, at least they are developing a specification that they can use if there might be a shortage of polymers or if polymers become scarce or just too expensive. States once leery of noise-reducing rubberized asphalt in open graded friction courses, due to negative experiences with failing binders in the ‘80s, are increasingly accepting the benefits of the elasticized asphalt in such applications. Many are taking a second look, maybe even a third look, at these mixes. When contractors are substituting tire rubber for polymers, they are saving the state’s department of transportation anywhere from $2 to $5 per ton of mix. It adds up. On big projects, you can save anywhere from $50,000 to $250,000 rather quickly. This could result in a fund-strapped agency, local or state, being able to complete one additional project per year than otherwise possible. Back in rural Ontario, where healthy skepticism and vocal opinions on most issues have never gone out of vogue, today there’s more than just the universal desire for more roadwork, but an actual enthusiasm for what that additional paving job should entail.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.185 | 0.097 |
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".