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Record W7084589772 · doi:10.46660/ijeeg.v15i1.464

Thermal Properties of Rocks and Environmental Sustainability

2024· article· en· W7084589772 on OpenAlexaff

Bibliographic record

VenueInternational Journal of Economic and Environmental Geology · 2024
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsSustainabilityGlobal warmingGeothermal gradientThermalSustainable developmentGeothermal energyGeothermal heatingClimate change

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.217
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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