TECHNICAL EFFECTS OF AIR COOLED BLAST FURNACE SLAG ON ASPHALT MIXTURES
Bibliographic record
Abstract
Air cooled blast furnace slag is a by product made of gradually air cooled molten blast furnace slag and is often stored in stockpiles near the iron mills and usually occupy a wide area around the iron mills. From this perspective the further use of these materials can have its own merits. Air cooled blast furnace slag can be used in asphalt mixtures because of their proper frictional properties. This research is conducted to investigate the effect of air cooled blast furnace slag on technical characteristics of asphalt mixtures and compare the results with the traditional asphalt mixtures. Two series of Marshal and Texas Boiling Tests were carried out in the laboratory. Three types of aggregates (air cooled blast furnace slag, siliceous gravel, lime gravel) with two types of dense graded and open graded were used. The frictional properties of the asphalt mixed with air cooled blast furnace slag materials were evaluated by using the British Pendulum, and the Sand Patch Method. Resistance to moisture was also determined in accordance to Texas Boiling Test. Results showed that by increasing percentage of air cooled blast furnace slag in asphalt mixtures, Marshall Stability, flexibility and skid resistance augmented. Also Texas Boiling Tests indicated that asphalt mixtures have a good resistance to stripping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".