Linking deformation and diffusion to develop a strain speedometer
Bibliographic record
Abstract
I present the first comprehensive study of strain-enhanced diffusion in plagioclase through a multiscale, theoretical and physical approach. First I argue from first principles that diffusion and deformation are both processes of breaking and reforming bonds that take energy in order to proceed. Deformation can enhance diffusion by supplying strain energy that facilitates breaking bonds and by mobilizing dislocations, which can act as pathways for atomic migration. Widely-used geologic tools such as geochronometers and geothermobarometers neglect deformation-enhanced diffusion, which may lower closure temperatures and reset ages, temperatures, and pressures, leading to erroneous values calculated based on static diffusion or no diffusion after conditions of interest. If deformation enhancement of element mobility is measurable, we may be able to calculate and account for the effects of deformation on these calculations. The connection between deformation and diffusion also provides an opportunity to link deformation with a time-dependent process and infer strain rate. Strain rate is an integral parameter in understanding crustal deformation and cannot be directly determined from the rock record in most cases. To evaluate strain enhanced diffusion in a common rock forming mineral, I combined field-scale, thin-section scale, and grain-scale strain and chemical measurements for major and trace elements in naturally deformed plagioclase phenocrysts from the San José Pluton, Peninsular Ranges Batholith, México. The multiscale approach made it possible to connect grain-scale strain with bulk-rock deformation through methods I developed. I used high spatial resolution, sensitive, in situ analyses using electron probe micro-analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) to correlate changes in shape (strain) and chemical composition (element mobility). I conclude that at low strain and low compositional contrast between primary growth zones measured, the effect of strain-enhanced element mobility, as predicted from my model, is too subtle to quantify using available analytical techniques. This means that for such a set of conditions, a static diffusion model is sufficient, and geothermobarometers and geochronometers can still be applied to yield accurate results. Future work should explore the relationships between different variables affecting strain enhanced element mobility, such as thermal history, strain, strain rate, and starting compositions, to refine the applicability of existing tools and the potential for new ones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".