Influence of the Mineralogical and Mortar Components on the Adherence of Some “Granites”
Bibliographic record
Abstract
Rock plate setting can be performed with metal inserts or by mortar adhesion. For mortar setting, the adhesion bond strength values, as a rule, should be above 1 MPa. In the present work, tests with eight types of “granite” tiles were performed to compare the adherence of five types of mortars. The rocks chosen were: Red Brasilia (syenogranite), Black Indian (migmatite), Green Labrador (charnockite), Black Sao Gabriel (hypersthene diorite), Rose Jacaranda (nebulitic migmatite syenogranite), Fantastic Blue (biotite monzogranite megaporphyritic serial gneissified), Grey Swallow (monzogranite) and Yellow Ornamental (garnet porphyroblastic gneiss), which do not have similar petrographic and sawability characteristics, thereby resulting in different initial roughness values of the plates obtained by breaking apart the blocks on the gangsaw machine, which use granulated steel as an abrasive element. The adherence of these rocks with the mortars was determined in the rough surface as well as in the polished surface by the pullout traction test, standardized for ceramics. The results showed that the mortar adhesion is related to roughness, to mineralogy and to the texture of these rocks. To verify this relationship, tensile bond strength tests were performed with the main mineral components of these rocks using single crystals with known optical orientation. Moreover, the microstructure study of the standard substrate/mortar/rock set was also performed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".