Field and Laboratory Characterization of Tire Derived Aggregate in Alberta
Bibliographic record
Abstract
Tire Derived Aggregate (TDA) is made by shredding scrap tires into 50 to 300 mm pieces. This material is lightweight and has higher permeability and thermal resistivity than soil. Because of these properties, TDA has been successfully used as a fill material in various highway construction projects in the United States and other countries. Moreover, recycling discarded tires has economic and environmental benefits, such as eliminating the need to store waste tires in landfills. In order to evaluate the performance of TDA as fill material in cold climates, a largescale field and laboratory experiment was performed in Edmonton, Alberta. The field test embankment, which used nearly 7,000 tons of discarded tires, is an 80 m instrumented test road and contains four sections: 1) TDA from Passenger and Light Truck Tires (PLTT), 2) TDA from Off-The-Road truck tires (OTR), 3) TDA from PLTT mixed with soil and 4) native soil (control section). Large-scale, one-dimensional laboratory compression tests were also performed to characterize the compression behavior of different TDA material. The paper presents the test results and the findings of the field instrumentation. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".