Composite Tiles Produced from Granite Dust and Tree Pruning Using a Sandwiched Method
Bibliographic record
Abstract
Projected by the United Nations, a global population surge to an estimated 9.6 billion by 2050 is anticipated, exerting substantial demands on Earth's finite natural resources for construction materials.This research endeavors to scrutinize and fabricate an innovative recycling approach for almond tree pruning as core materials, integrated with granite dust, employing cement as a binding agent in the fabrication of composite tiles measuring 190×98mm.Adhering to ISO standards, the resultant product underwent rigorous evaluation encompassing density, water absorption, flexural strength, compressive strength, and thermal conductivity.The composite tiles manifest a density spectrum spanning from 1.05 to 1.89 g/cm³ , coupled with a water absorption capacity ranging from 8.25% to 33.89%.The experimental findings reveal that the integration of almond tree pruning amidst granite dust leads to a reduction in the flexural and compressive strengths of the composite tiles.Nevertheless, Sample A (comprising 78% granite dust, 2% tree pruning, and a 20% cement admixture) attains the pinnacle of flexural strength at 1.256 MPa and compressive strength at 0.421 MPa, thus representing the optimal blend ratio.Additionally, the thermal conductivities of these composite tiles exhibit a variance from 0.022 to 0.3802 W/mK, rendering them ideally suited for applications requiring low-load bearing, insulation, lightweight properties, and energy-efficient construction tiles.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".