Estimation of saturation index for the precipitation of secondary minerals during water-rock interaction in granite terrains
Bibliographic record
Abstract
The precipitation of secondary minerals not only controls the evolution of components in groundwater,but also has a significant influence on the migration and retardation of radionuclide in water-rock systems.Under ambient conditions,however,a driving force of oversaturation is required to break the nucleation barrier for the precipitation and growth of secondary minerals,and different degrees of over-saturation are required for the precipitation of different secondary minerals.In this study,saturation indices(SI) for the precipitation of various minerals that would form as secondary phases under ambient conditions,are estimated by using the geochemical simulation software of PHREEQC 2.15 and the database of llnl.dat based on the geological and hydro-geochemical data from Japanese and Canadian granite regions.The results indicates that calcite is subjected to dissolution and precipitation due to its strong chemical activity,and is expected to precipitate at SI≈0.5.Goethite is considered to be a Fe sink in groundwater,the calculated SI value is closely associated with the pe value,and precipitates at SI = 4.0 ± 0.5.Combined with thermodynamic constrains on secondary clay minerals,the SI values for the precipitation of kaolinite,illite,Ca-montmorillonite and Na-montmorillonite are estimated to be 4.0 ± 0.5,4.5 ± 0.5,4.3 ± 0.5 and 4.3 ± 0.5,respectively.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".