Thermal Properties of Rocks and Environmental Sustainability
Bibliographic record
Abstract
Sustainable environments and the pursuit of alternative energy supplies are central to modern societies. At the same time the global warming is emerging as a serious issue for all nations. In contrast to the physical and mechanical properties of rocks, the thermal properties of rocks provide information on its potential for alternative thermal energy sources. This study examines the earlier research studies on the evaluation of rocks' thermal properties, with a particular emphasis on geothermal potential, dimension stone thermal comfort, and indirect evaluation utilizing characteristics including porosity, moisture content, p-wave velocity, and mineral composition. The transient approach and steady state technique were used to evaluate thermal characteristics of rocks. Given that Pakistan is among the nations adversely affected by global warming, it is imperative to investigate alternative energy sources and sustainable materials. This study tries to provide directions to enhance the knowledge base for future research to analyze the thermal properties of rocks originating from Pakistan and how strategically these rocks may be utilized to lessen global warming through environment sustainability and zero carbon emissions and to achieve sustainable development goals. Keywords: Environment sustainability, thermal properties of rocks, dimension stones, thermal comfort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".