Utilização de ensaios tecnológicos como auxílio na interpretação do polimento de rochas ornamentais
Bibliographic record
Abstract
The polishment is the main type of finishing performed in slabs and tiles of rocks, which is done by the friction generated in the rotational motion of abrasive elements disposed in polishing heads, under pressure, against the rock surface. The rock is an active element in the process, therefore the knowledge of its properties, can contribute to improving the understanding of this important stage of processing. Three petrographic types of rocks with distinct textural and structural, were selected for this study, in order to compare the results of characterization tests and try to relate intrinsic characteristics which most influenced the action of wear, supporting information for interpretation of the polishing process. The chosen materials were charnockite, monzogranite and gneiss, known commercially Verde Labrador, Cinza Castelo and Preto Indiano. Among the mechanical-physical tests, the results of the uniaxial compression strength, density, water absorption, coefficient of linear thermal expansion and propagation of longitudinal waves show no direct correlation with experimental results of polishing. On the other hand, the porosity, wear resistance and knoop hardness show apparent direct correlation with the rock polishing.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".