Methodology for solute characterization of fluid inclusions by petrographic and SEM/EDS complementary analysis
Bibliographic record
Abstract
A cost-effective, simple, and time-efficient method to determine the bulk composition of fluid inclusions is evaporate mound analysis. This method is semi-quantitative and determines inclusion composition by integrating SEM imaging with energy-dispersive analysis of precipitates, or mounds, produced by thermal decrepitation of fluid-inclusions. The method is applicable to magmatic-hydrothermal systems where fluid inclusions contain solute ions (e.g., Na, K, Ca, Cl, F). In order to assess the application of this method for evaluating hydrothermal evolution and metal fertility with regards to intrusion-related mineralisation, a test study is being conducted on the large (7800 km2) and variably mineralized (e.g., Sn, W, Cu, U, Mo, Ta) South Mountain Batholith (SMB) of Nova Scotia. Decrepitate mounds were analyzed using a LEO 1450VP (SEM) imaging system linked to an Oxford X-Max 80 mm2 SDD detector energy-dispersive detector. Based on decrepitating over a range of temperatures, from 325°C to 500°C, it appears that T = 500°C is optimal to produce large, well-shaped, and readily identifiable mounds. To optimize analysis time and, hence, increase research efficiency while maintaining result accuracy, data were collected with 5, 10, and 30 second acquisition times. The number of analyses required to produce representative results was also tested by comparing the results for 4, 8, 16, 32, and 64 mound analyses for individual samples. Results indicate that optimal procedures require multiple (N = 12) point-mode analyses on individual decrepitate mounds to substantiate mound heterogeneity, and that in order to accurately reflect in-situ fractionation a single, 10 second raster-mode analysis is the best approach. These optimal analytical protocols are being applied to a regional study of the SMB to determine their suitability as mineral fertility indicator and/or vector to ore mineralisation. This test case is the first of its kind conducted on a batholithic scale, with the resulting methodological protocols being readily exportable for the mineral fertility assessment of other regions. Data interpretation protocols integrate a granitic petrographic alteration index, fluid inclusion types, density and evaporate mound chemistry. Samples are chosen such that the entire batholith may be assessed, with all mapped lithologies represented. Fluid compositions determined thus far include brines with 5-20 % fluorine, which is quantitatively indeterminable using other methods, and has been linked as primary control on the transport and deposition of ore in porphyry- and greisen-style mineralisation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".