Thermal Separation as an Alternative to Centrifugation for Two-Liquid Partitioning Studies
Bibliographic record
Abstract
Determining experimental partition coefficients in two-liquid systems is challenging, partly because completely separating immiscible phases is difficult [1] .Sufficient phase separation can lead to cross-contamination between phases during trace element analysis that mute the measured partition coefficients.This is particularly challenging for low viscosity melts, such as fluorides, where quench effects complicate the textural interpretation of experimental run-products [1] .High temperature centrifugation can overcome these problems [2] , however, such equipment is not widespread.We propose an alternative approach using a vertical tube furnace, a common feature of experimental petrology laboratories.Our exploratory experiments have employed the small (<10 C/mm) thermal gradients intrinsic to a vertical tube furnace to separate REE-doped immiscible fluorosilicate-silicate melts via thermodiffusion (Fig. 1).We employed starting compositions from [1], allowing direct comparison to previous work.Preliminary results indicate the fluorosilicate phase concentrates to the hot end of the capsule, and liquidus fluorite resides within the fluorosilicate phase (Fig. 1).Importantly, the small thermal gradients used do not induce significant major-element gradients internal to each melt.Work is ongoing to determine trace element compositions of each phase, confirm the timescales required to achieve steady-state, and extend the range of studied melt compositions.Fluorosilicate-silicate REE partition coefficients (D REE FM-SM ) obtained from well separated melts where cross-contamination during analysis can be avoided, will provide new constraints on genetic models of A-type granite REE deposits, such as Strange Lake, QC, Canada.Strange Lake contains evidence for an immiscible fluoride melt hosting >40 wt% REEs that may be important to ore formation [3] .Existing values of (D REE FM-SM ) do not explain the formation of such a REE-enriched melt, motivating further studies and technique development, such as that proposed here, to improve our ability to probe these systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".