Utilizing Heat of Wetting to Estimate Physical Properties of Tuff
Bibliographic record
Abstract
During characterization efforts of complex sites and geologies, it is important to estimate material properties efficiently and robustly. We present data and modeling related to the heat of wetting process during spontaneous imbibition, as observed in zeolitic tuff. The heat of wetting is due to adsorption of liquid water and water vapor to an oven-dry core sample and results in an observable temperature rise. The fitting of numerical models to imbibition observations allows simultaneous constraint of single-phase (porosity, permeability), two-phase (van Genuchten m and alpha), thermal (thermal diffusivity), and transport (tortuosity) properties from a single imbibition test. Petrographic analysis informs how microstructure connectivity and pore-lining phases affect the imbibition process. Estimating multiple properties simultaneously from a single test on a core sample helps ensure consistency in interpreted material properties. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 (SAND2023-07021A).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".