Characterization of Buner marble from Pakistan for construction purposes
Bibliographic record
Abstract
The exploration and management of abundant economic mineral resources of Pakistan, particularly the vast marble deposits in the northwestern region, hold immense potential for driving economic growth. The use of marble in the construction industry faces extensive challenges such as undeveloped mining processing methods, incomplete understanding of marble qualities, undefined selection criteria for suitable varieties, and the environmentally harmful consequences of excessive waste production. This research developed a laboratory investigation protocol to characterize distinct marble deposits in Buner, Pakistan, each offering unique compositions and petrographic features. Three marble varieties were identified including pure calcitic (over 90% calcite) with low silica content (0.1% to 2.5%); impure calcitic (non-carbonate minerals up to 20%) with 19.8% silica and 31% lime; and pure dolomite (over 20% dolomite) with 29% lime and 23% magnesium oxide. The distinctive petrographic features of the marble deposits, such as equigranular structures, subhedral to anhedral grains, granuloblastic textures, and schistosity in impure calcitic, as well as luster-displaying dolomite in pure dolomite, provide valuable insights into their geological characteristics. Furthermore, the physical properties of the marble types exhibit correlations with their compressive and tensile strengths. Notably, the low specific gravity, water absorption, and porosity of the investigated marble result in high strength values. The average compressive strength was found to be 31 MPa for pure calcitic, 35 MPa for impure calcitic, and 59 MPa for pure dolomite marble. Likewise, the tensile strengths measured 6 MPa, 7 MPa, and 9 MPa, respectively. While the investigated marble types prove to be excellent choices for dimension stone applications, it is crucial to note that they do not meet the standards required for cement production and paint manufacturing. This research contributes to the understanding of Pakistan's marble resources, refined processing methods, and sustainable construction practices.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".