Pressure–temperature–time controls on shock vein formation within the Steen River impact structure
Bibliographic record
Abstract
ABSTRACT Thermodynamic modeling has been applied to determine pressure–temperature–time conditions leading to shock vein formation during the passage of a natural shock wave generated by hypervelocity impact. The approach is novel in considering both shock front and rarefaction pressures, as well as simultaneously forming and cooling the shock veins via two‐dimensional steady‐state conduction. Model results are tested using shock veins developed in granitic rocks that constitute the central uplift of the Steen River impact structure in Canada. Here, two variants of majoritic garnet were generated in different settings: (1) along the margins of shock veins due to pargasite and biotite breakdown (accompanied by maskelynite formation), and (2) within the originally molten shock vein matrix as newly grown crystals. We determine that during shock vein formation, the shock front pressure and wave width at the reconstructed sample location were 18 GPa and 830 m, respectively, with a dwell time of 160 ms. Intra‐vein melting at 2150°C was attained within 1 μs. Melt cooled to the solidus in 150 ms following shock front passage. Majoritic garnet formation was facilitated by the high temperatures realized within the veins as a result of frictional melting that accompanied shock loading. The calculated pressure–temperature–time ( P – T – t ) path provides constraints on the formation conditions of majoritic garnet at Steen River. The model results independently support previously determined P – T conditions based on mineral stability fields. The vein margin garnets (35–39 mole% majorite) and maskelynite formed first under higher P – T conditions for a longer duration (36 ms). The matrix garnets (11–22 mole% majorite) crystallized from melt under lower P – T conditions and for a shorter duration (22 ms). Our results indicate that shock pressure alone should not be used as a basis for shock classification. Instead, the interplay between pressure and temperature with time and the duration of shock immersion (dwell) must be considered.
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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 | 0.001 | 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".