Towards coupled geodynamic and hydrothermal numerical rift models of sediment-hosted Copper and Zinc deposits
Bibliographic record
Abstract
Many large sediment-hosted base metal deposits occur in failed continental rifts and the passive margins of successful rifts, e.g., in the MacArthur Basin, Australia, and the Selwyn Basin in Canada. Continental rifts and their margins provide a specific mix of elevated temperatures and heat flows, fault networks to facilitate fluid flow, sediment supply from the rift shoulders, and ocean water contributing pelagic sediments and sulfate. The large-scale geodynamics thus provide the necessary ingredients for metal leaching and deposition to occur on a variety of spatial and temporal scales. To understand the geodynamic controls on ore formation, we are therefore coupling the geodynamic code ASPECT1,2 (coupled to the landscape evolution model FastScape3,4) with the hydrothermal fluid flow code CSMP++5,6 to include realistic pressure, temperature, and heat flow conditions, as well as permeability and sediment distributions, for fluid flow and metal leaching/deposition. This coupled workflow crosses temporal scales of millions of years to years and spatial scales of hundreds of kilometers to meters. We present preliminary results from geodynamic modelling of large-scale continental rifting and hydrothermal simulations at specific snapshots of the upper 10 km of crust of this large-scale geodynamic evolution, showing the effect of rift duration, adjacent craton thickness, and erosion efficiency on sediment-hosted Cu and Zn deposits. 1Kronbichler et al. (2012). GJI191(1), 12–29. 2Heister et al. (2017). GJI, 210(2), 833–851. 3Neuharth et al. (2022). Geology, 50(3), 361–365. 4Braun & Willett (2013). Geomorphology, 180-181, 170-179. 5Weis et al. (2014). Geofluids, 14(3), 347–371. 6Rodríguez et al. (2021). GCubed, 22(6).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".