Bibliographic record
Abstract
Since the early 1980s, resonant rubblization has evolved from a process used to prepare existing portland cement concrete (PCC) pavement for removal to a technique for converting PCC pavement into an unbound base or subbase. The original equipment, developed by Mr. Ray Gurries in Nevada, uses a vibrating steel beam to apply high frequency, low amplitude loads through a breaking shoe that contacts the surface of the concrete pavement as the equipment moves along the pavement. The PCC fractures throughout its thickness and breaks the bond between any distributed steel and concrete, making the process of removal and hauling much more efficient than traditional methods using drop hammers. While this equipment was originally developed to expedite the removal of PCC pavement, resonant rubblization is now used mostly as a technique for preparing an existing PCC pavement for overlay with hot mix asphalt (HMA). Slab movement that causes reflection cracking in HMA overlays is eliminated, and a stable foundation for construction of a new HMA pavement is created without having to remove or further process the existing material. In 1986, the New York State Department of Transportation was the first agency to use resonant rubblization as a method for preparing failed PCC pavements for overlaying with HMA. Since then, resonant rubblization has been successfully used in the rehabilitation of jointed plain, jointed-reinforced, and continuously reinforced PCC pavements ranging from city streets to Interstate highways, and on military and commercial airports. Resonant rubblization has been used in 37 U.S. states and is now being used in two Canadian provinces and several countries in Asia, Europe, and South America.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".