Determining Fluid Compositions in the H<sub>2</sub>O‐NaCl‐CaCl<sub>2</sub> System with Cryogenic Raman Spectroscopy: Application to Natural Fluid Inclusions
Bibliographic record
Abstract
Previous cryogenic Raman spectroscopic analysis of H2O‐NaCl‐CaCl2 solutions has identified the Raman peaks of various hydrates of NaCl and CaCl2, and established a linear relationship between Raman band intensity of the hydrates and the composition of the solution (NaCl/(NaCl+CaCl2) molar ratio, or XNaCl) using synthetic fluids, which created the opportunity to quantitatively determine the solute composition of aqueous fluid inclusions with cryogenic Raman spectroscopy. This paper aims to test the feasibility of this newly established method with natural fluid inclusions. Twenty‐five fluid inclusions in quartz from various occurrences which show a high degree of freezing during the cooling processes were carefully chosen for cryogenic Raman analysis. XNaCl was calculated using their spectra and an equation established in a previous study. These inclusions were then analyzed with the thermal decrepitation‐SEM‐EDS method. The XNaCl values estimated from the two methods show a 1:1 correlation, indicating that the new, non‐destructive cryogenic Raman spectroscopic analysis method can indeed be used for fluid inclusion compositional study.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".