VOLUME CHANGES IN FLUID INCLUSIONS PRODUCED BY HEATING AND PRESSURIZATION: AN ASSESSMENT BY FINITE ELEMENT MODELING
Bibliographic record
Abstract
Recent advances in using the hydrothermal diamond anvil cell (HDAC) to measure homogenization temperatures of inclusions trapped at high pressure have created a need to better understand changes in elastic volume of fluid inclusions experiencing high internal and external pressures. We have used finite element modeling to explore volume changes of fluid inclusions as a function of shape and distance from the free surface at a sample’s edge as an aid in understanding their behavior in HDAC studies. We have modeled a variety of oblate and prolate ellipsoids, as well as a disk that has the same cross-section as a negative crystal in quartz and two right cylinders. All of our models have an axisymmetric geometry and assume linear isotropic elasticity. We find that the percent change in volume of an inclusion is primarily a function of the aspect ratio of the inclusion. The presence or absence of corners and the sharpness of internal corners also affect the volume change, but to a lesser extent. Distance to a free surface is only significant for inclusions that are very close to the free surface. This effect is most pronounced for an oblate ellipsoid oriented with its long dimensions parallel to the free surface. For microthermometric studies of fluid inclusions at 1 atm, the changes in elastic volume due to increases in internal pressure are negligible. However, for HDAC studies, where the application of confining pressure allows more extreme conditions to be obtained, changes in elastic volume can be significant, but can be predicted using finite element models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".