Possibilities of production of wear resistant construction elements by processing of Serbian basalt
Bibliographic record
Abstract
This paper covers the possibilities of domestic basalt processing by advanced ceramics and melting treatment. We used basalt from the locality of 'Donje Jarinje'(Leposavic) and 'Vrelo' (Kuršumlija) as a raw material. Laboratory examinations of the possibilities of basalt processing have been made mostly in 'Centre for Manufacturing of Advanced Ceramics and Nanomaterials', Queen's University (Canada). In processing, we applied two, in essence different processes. One included milling, pressing and sintering, and the other melting and casting. Before sintering, basalt aggregate was milled in the powder then mixed with the additives and after that, isostaticaly pressed under pressure of 225 MPa. Casting as a method of basalts processing consists of melting of the aggregate in an electric resistant furnace, pouring into the mold and cooling of the castings, with relaxation of internal stress. Experimental results obtained in these examinations show that the casting method of treating the basalt gives more possibilities in a matter of shapes and dimensions of pieces, but the mechanical characteristics of final products were approximately the same. Wear resistance was high in both cases, considering that cast pieces have a slightly better wear resistance. Pieces received by advanced ceramics process show porosity, and in some cases, this characteristic can be the limitation in final products application.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".