Refinement of the Hot-Mix Asphalt Ignition Method for High-Loss Aggregates
Bibliographic record
Abstract
Four methodologies for determining the asphalt content of mixtures containing high-loss aggregates in the ignition furnace were evaluated: the standard method using the Thermolyne furnace (control), the Troxler NTO infrared furnace, the Ontario method, and a Tempyrox glass-cleaning oven. Six aggregate sources with high ignition furnace aggregate corrections were obtained from around the country: four dolomites, a basalt, and a serpentine/chlorite. Calibration factors were determined for each method at optimum asphalt content. Additional samples were then tested at optimum plus 0.5% asphalt content, and the measured asphalt content was calculated by using the correction factor determined for that method and aggregate source. The Tempyrox Pyro-Clean furnace, commonly used for cleaning laboratory glassware, produced the lowest aggregate correction factors. The standard method and the Ontario method, both using the Thermolyne ignition furnace, produced the smallest bias or error in measured asphalt content. The standard deviation of the corrected asphalt contents for these high-loss sources was higher than the within-laboratory standard deviation reported for AASHTO T308. The only exception was the Alabama source using the standard method. The Ontario method and Tempyrox oven generally reduced the variability of asphalt content measurements for high-loss aggregates. None of the methods evaluated statistically reduced aggregate breakdown on the nominal maximum aggregate size and 4.75-mm sieves. The Ontario method significantly reduced, but did not eliminate, aggregate breakdown on the 0.075-mm sieve. The Ontario method is the best method for immediate implementation for determining the asphalt content by the ignition method for high-loss aggregates.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".