Laboratory Evaluation of Partial Depth Patching Materials for Use in Winter Climates
Bibliographic record
Abstract
Over the course of their service life, concrete pavements undergo significant traffic and climatic loads, which lead to a gradual accumulation of damage. This accumulation of damage and distress comes from the effects of changing weather conditions (e.g., temperature, moisture) and continuous vehicular traffic. Repeated environmental and traffic loading leads to cracking and spalling of the concrete at the joint edges. States in the northern portion of the United States and the provinces of Canada have climates that fluctuate greatly in temperature throughout the seasons. Greater temperature differentials cause greater deflections in rigid pavements; these deflections lead to more prevalent spalling and a greater need for partial depth repair. Many U.S. state departments of transportation (DOTs) use partial depth repair as routine practice to maintain concrete pavements (e.g., the DOTs of Minnesota, North Dakota, South Dakota, Idaho, Montana, Washington, and Wisconsin). Enhanced acceptance criteria of rapid set cementitious materials for use in partial depth repair are needed. The purpose of this study was to investigate laboratory tests, recommended by ASTM C928 and others, for inclusion in the acceptance specifications for patching materials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".