Thermal conductivity of Triassic evaporites
Bibliographic record
Abstract
SUMMARY Evaporites occur in various geological environments: sedimentary basins, orogenic belts, where they often act as tectonic decoupling layers, and as top-seals in hydrocarbon fields. In all cases, they affect the temperature distribution in the upper crust, as their thermal conductivity is relatively higher with respect to other sedimentary rocks. High heat conduction through evaporites enhances the geothermal gradient above the evaporitic layer and decreases it below, with potential consequences for surface heat flow, depth of the brittle–ductile transition and low-enthalpy geothermal exploitation. An accurate determination of their thermal conductivity is therefore necessary. We estimate the thermal conductivity of evaporitic rocks with a two-pronged method. First, an exhaustive review of the literature allows the determination of the conductivity for the main evaporitic minerals and of their variation with temperature. Secondly, in order to assess the effects of compositional variability, we select six samples of Triassic evaporites from the Apennines (from both outcrops and boreholes) and measure their mineralogical composition and thermal conductivity. The composition has a strong effect on conductivity, which goes from 5 W m–1 K–1 when anhydrite or dolomite are volumetrically predominant, to 2 W m–1 K–1 when gypsum is predominant. We also use various mixing models (where the rock conductivity is estimated from the mineralogical composition) and find sufficient agreement between measured and predicted values to justify the use of such models when direct measurements are not available. Finally, as an illustrative example of the thermal consequences of evaporites in the upper crust, we model the variations of temperature and surface heat flow caused by the occurrence of evaporitic layers of different thickness. The results show that the effects on crustal geotherms and the distribution of seismicity can be significant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.029 | 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 teacher head, 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".