Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing
Bibliographic record
Abstract
Abstract Connecting the surface expression of impact crater‐related lithologies to planetary or regional subsurface compositions requires an understanding of material transport during crater formation. Here, we use imaging spectroscopy of six clast‐rich impact melt rock outcrops within the well‐preserved 23.5‐Ma, 23‐km diameter Haughton impact structure, Canada, to determine melt rock composition and spatial heterogeneity. We compare results from outcrop to outcrop, using clasts, groundmass, and integrated clast‐groundmass compositions as tracers of transport during crater‐fill melt rock formation and cooling. Supporting laboratory imaging spectroscopy analyses of 91 melt‐bearing breccia and clast samples and microscopic X‐ray fluorescence elemental mapping of cut samples paired with spectroscopy of identical surfaces validate outcrop‐scale lithological determinations. Results show different clast‐rich impact melt rock compositions at three sites kilometers apart and an inverse correlation between silica‐rich (sandstone, gneiss, and phyllosilicate‐rich shales) and gypsum‐rich rocks that suggests differences in source depth with location. In the target stratigraphy, gypsum is primarily sourced from ~1‐km depth, while gneiss is from >1.8‐km depth, sandstone from >1.3 km, and shales from ~1.6–1.7 km. Observed heterogeneities likely result from different excavation depths coupled with rapid quenching of the melt due to high content of cool clasts. Results provide quantitative constraints for numerical models of impact structure formation and give new details on melt rock heterogeneity important in interpreting mission data and planning sample return of impactites, particularly for bodies with impacts into sedimentary and volatile‐bearing targets, e.g., Mars and Ceres.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".