GEM-SELEKTOR GEOCHEMICAL MODELING PACKAGE: TSolMod LIBRARY AND DATA INTERFACE FOR MULTICOMPONENT PHASE MODELS
Bibliographic record
Abstract
The development of highly accurate and computationally efficient modeling software based on Gibbs energy minimization (GEM) makes it possible to thermodynamically simulate geochemically realistic subsurface fluid-rock interaction processes. This involves consideration of non-ideal multicomponent-multiphase systems that include dilute to concentrated aqueous electrolyte solutions, mineral solid solutions, supercritical fluids, silicate and metal melts, and sorption and ion exchange phases. Predicting the stability and thermodynamic properties of non-ideal solution phases over wide ranges of pressures and temperatures requires that theoretically sound and sufficiently accurate equation of state and activity models are used within the GEM framework. The variety of such models calls for a novel, flexible, and computationally efficient code architecture that supports a wide range of models of non-ideal mixing with different mathematical structures and input data. Here, we introduce the TSolMod C++ class library for equation of state and activity models, implemented within the GEMS3K solver of geochemical equilibria as part of the GEM-Selektor code package (http://gems.web.psi.ch). Essential features of the TSolMod library include a generic and flexible model parameter setup, computationally efficient data exchange with the GEM algorithm, and a straightforward extensibility with any new models of mixing. The current version of TSolMod features a comprehensive selection of fluid, gas, liquid, and solid solution models of interest for geochemical, petrological, material science, and chemical engineering applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.024 |
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".