Identification of Fluid Inclusion Solid Phases Using A Focused Ion Beam Scanning Electron Microscope With Energy Dispersive Spectroscopy: Implications For Interpreting Microthermometric Data and Composition of Fluids In SN (-W-Mo) Deposits
Bibliographic record
Abstract
Abstract A feature of many granite-related mineral deposits is the presence of highly saline fluid inclusions that contain abundant daughter minerals, the identity and chemistry of which hold important information for the interpretation of the chemistry of mineralizing fluids in such environments. A focused ion beam (FIB) coupled with a scanning electron microscope (SEM) and energy-dispersive spectroscope (EDS) was used to identify daughter minerals within such fluid inclusions in fluorite from the Mount Pleasant Sn (-W-Mo-In) deposits, New Brunswick, Canada. The technique can be used to obtain quantitative compositional information for solid phases in fluid inclusions for which the optical properties have previously been studied, but whose identification remains equivocal. In addition, for solid-rich fluid inclusions, this technique enables identification of solids that are obscured by other solids and are not readily visible using optical microscopy. Methods such as Raman spectroscopy and SEM-EDS analysis of broken surfaces have limited success in mineral identification in these inclusions due to strong fluorescence in fluorite and an inability to correlate solids with their optical properties, respectively. The FIB-EDS analyses of the daughter minerals and precipitates formed by evaporation of the liquid in the inclusions show that the complex inclusions from Mount Pleasant contain brines with high concentrations of Na, Fe, K, Mn, Pb, Zn, and Cl; daughter minerals comprise halite and a variety of Fe-K(-Mn) chloride hydrates, and less frequently, Pb chlorides and Sn-bearing phases. The precipitates contain similar phases, although in general they are more K-rich than the daughter minerals. Unequivocal identification of most solid phases was not possible due to inaccuracies inherent in the FIB-EDS chemical analyses and a lack of Raman spectra for many K-Fe-Mn chloride hydrate minerals. Our data indicate that square (“cubic”) solids in such inclusions are not always halite, and that misidentification of halite could result in erroneous salinity estimates, which could lead to inaccurate interpretations of fluid composition using techniques such as laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS).
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".