Features of interaction of organic binder and slag filler
Bibliographic record
Abstract
1. Gustafson K. Road icing on different pavements structures. Investigation at Fest–Field Lincoping 1976–1980 / K. Gustafson // Rapp. Statens vogjch tratikinst. – 1981. – 216A(12). – 174 p. 2. Untersuchungen zum Griffigkeitsverhalten von Splittmastixasphalt–Deckschichten / S. Huschek, J. Dames, J. Kanyi, J. Lindner // Forschung, Strassenbau und Strassenverkchrstechnik. – 2002. – 837. – P. 1–53. 3. Ahmedzade P. Evaluation of steel slag coarse aggregate in hot mix asphalt concrete / P. Ahmedzade, B. Sengoz // Journal of Hazardous Materials. – 2009. – 165 (1–3). – P. 300–305. DOI: dx.doi.org/10.1016/j.jhazmat.2008.09.105. 4. Asi I. M. Use of steel slag aggregate in asphalt concrete mixes / I. M. Asi, H. Y. Qasrawi, F. I. Shalabi // Canadian Journal of Civil Engineering. – 2007. – 34(8). – 902–911. DOI: dx.doi.org/10.1139/107-025. 5. Aide au choix des couches de roulement vis-à-vis de l’adherence / [G. Aussedat, A. Barbiero, A. Baudon et al.]. // Revue Generale des Routes. – 2003. – 813. – P. 59–61. 6. Environmental impacts of steel slag reused in road construction: A crystallography and molecular (XANES) approach / [P. Chaurand, J. Rose, V. Briois, L. Olivi et al.]. // Journal of Hazardous Materials. – 2007. – 139(3). – P. 537–542. DOI: dx.doi.org/10.1016/j.jhazmat.2006.02.060. 7. Dependence of the recycled asphalt mixture physical and mechanical properties on the grade and amount of rejuvenating bitumen // D. Čygas, D. Mučinis, H. Sivilevičius, N. Abukauskas // The Baltic Journal of Road and Bridge Engineering. – 2011. – 6(2). – P. 124–134. DOI: dx.doi.org/10.3846/bjrbe.2011.17. 8. Deniz D. Evaluation of expansive characteristics of reclaimed asphalt pavement and virgin aggregate used as base materials / D. Deniz, E. Tutumluer, J. S. Popovics // Transportation Research Record. – 2010. – 2167. – P. 10–17. DOI: dx.doi.org/10.3141/2167–02. 9. Emery J. Stylink polymer modified asphalt cementpavement performance evalution / J. Emery // Geotechnical Engineering Limited (JEGEL). – 1999. – 12. – P. 1–27. 10. Hassan H. F. Laboratory evaluation of hot-mix asphalt concrete containing copper slag aggregate / H.F. Hassan, K. Al-Jabri // Journal of Materials in Civil Engineering. – 2011. – 23(6). – P. 879–885. DOI: dx.doi.org/10.1061/(ASCE)MT.1943–5533.0000246. 11. Hunt L. Steel Slag in Hot Mix Asphalt Concrete. Final Report State Research Project #511 / L. Hunt, G.E. Boyle. – Oregon Department of Transportation. USA. [Electronic reference]. – 2000. – 19 p. – Access mode: http://www.oregon.gov/ODOT/TD/TP_RES/. 12. Li W. Laboratory test study on asphalt concrete with steel slag aggregates / W. Li, P. Sun, C. Zhang // Applied Mechanics and Materials. – 2012. – 152–154. – P. 117–120. 13. Sivilevičius, H. 2011. The use of constrained and unconstrained optimization models in gradation design of hot mix asphalt mixture / H. Sivilevičius, V. Podvezko, S. Vakrinienė // Construction and Building Materials. – 2011. – 25(1). – P. 115–122. DOI: dx.doi.org/10.1016/j.conbuildmat.2010.06.050. 14. Use of steel slags in automobile road construction / K. Krayushkina, O. Prentkovskis, A. Bieliatynskyi, R. Junevičius // Transport. – 2012. – 27(2). – P. 129–137.
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 | 0.000 | 0.000 |
| 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) | 0.007 | 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".