Freezing and melting behaviors of H<sub>2</sub>O‐NaCl‐CaCl<sub>2</sub> solutions in fused silica capillaries and glass‐sandwiched films: implications for fluid inclusion studies
Bibliographic record
Abstract
Abstract Fluid inclusions of the H2O‐NaCl‐CaCl2 system are notorious for their metastable behavior during cooling and heating processes, which can render microthermometric measurement impossible or difficult and interpretation of the results ambiguous. This study addresses these problems through detailed microscopic examination of synthetic solutions during cooling and warming runs, development of methods to enhance nucleation of hydrates, and comparison of microthermometric results with different degrees of metastability with values predicted for stable conditions. Synthetic H2O‐NaCl‐CaCl2 solutions with different NaCl/(NaCl + CaCl2) ratios were prepared and loaded in fused silica capillaries and glass‐sandwiched films for microthermometric studies; pure solutions were used with the capillaries to simulate fluid inclusions, whereas alumina powder was added in the solutions to facilitate ice and hydrate crystallization in the sandwiched samples. The phase changes observed and the microthermometric data obtained in this study have led to the following conclusions that have important implications for fluid inclusion studies: (i) most H2O‐NaCl‐CaCl2 inclusions that appear to be completely frozen in the first cooling run to −185°C actually contain large amounts of residual solution, as also reported in some previous studies; (ii) inability of H2O‐NaCl‐CaCl2 inclusions to freeze completely may be related to their composition (low NaCl/(NaCl + CaCl2) ratios) and lack of solid particles; (iii) crystallization of hydrates, which is important for cryogenic Raman spectroscopic studies of fluid inclusion composition, can be greatly enhanced by finding an optimum combination of cooling and warming rates and temperatures; and (iv) even if an inclusion is not completely frozen, the melting temperatures of hydrohalite and ice are still valid for estimating the fluid composition.
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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 | 0.000 |
| 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".