Autocatalytic model of oscillatory zoning in experimentally grown<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mo>(</mml:mo><mml:mi mathvariant="normal">Ba</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant="normal">Sr</mml:mi><mml:mo>)</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">SO</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>solid solution
Bibliographic record
Abstract
Oscillatory zoning (OZ) is a phenomenon common to many natural minerals whereby the mineral composition varies more or less regularly from the core of the crystal to its rim. Oscillatory zoned barite-celestite (Ba,Sr)SO4 crystals are one of the very few examples of the OZ phenomenon that were obtained under controlled laboratory conditions. It is known that such crystals can be synthesized by precipitation from an aqueous solution during counterdiffusion in a gel column connecting two reservoirs. We present here a model of oscillatory zoning in such a binary solid solution grown from an aqueous solution. By expanding on a previously suggested model, we obtain oscillatory dynamical solutions for two limit cases: the growth of a flat crystal face and the growth of a spherical crystallite. We consider an autocatalytic dependence between the crystal growth rate and the crystal surface composition. The oscillatory patterns then arise as a kinetic effect due to the coupling between the diffusion field around the crystal and the fast crystal growth under far-from-equilibrium conditions. The effects of fluctuations in the aqueous solution concentrations are also considered. It is shown that they may lead to noisy oscillatory patterns.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".