Craton control on sediment-hosted metal deposits in continental rifts
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 in the Selwyn Basin (Canada). The large-scale geodynamics control the parameters involved in metal leaching and deposition on smaller spatial and temporal scales. Such parameters include 1) the supply of syn-rift siliciclastic sediments from the rift shoulders to form potential source rocks, 2) elevated temperatures and heat flows supporting metal leaching by fluids, 3) fault networks facilitating hydrothermal fluid flow, and 4) organic rich siliciclastic host rocks. Analysis of the occurrence of base metal deposits and lithospheric thickness has shown that the majority of deposits are located within 200 km of a craton edge (Hoggard et al. 2020). Numerical models have also shown that a craton close enough to an incipient rift controls the asymmetry of the rift system (Andres-Martinez 2016). We therefore investigate how the presence of a craton controls the development of environments favorable for metal leaching and deposition in rift basins. To this end, we use the geodynamic code ASPECT (Kronbichler et al. 2012; Heister et al. 2017) coupled to the landscape evolution model FastScape (Braun and Willett 2013; Neuharth et al. 2022) to model 2D rift systems from inception to break-up in the presence of a craton. With these high-resolution (~150 m) simulations, we investigate the relationship between craton distance and thickness and the area of potential source rock and host rock, where metals could be leached and deposited, respectively. We subsequently analyse the co-occurrence of such source and host rock and potential faulting events connecting them (e.g., Rodríguez et al. 2021). Preliminary results show that the close presence of a craton leads to predominantly asymmetric systems with a narrow craton-side margin (as shown by Andres-Martinez 2016). A craton also increases the amount of potential source and host rock. Whereas the amount of source rock increases with distance between craton and rift, the total area of potential host rock decreases. Furthermore, the co-occurrence of source, host and faults in one subbasin is rare. Andres-Martinez, M. 2016. PhD thesis, Royal Holloway University of London. Braun, J. and S. D. Willett. 2013. Geomorphology 180–181: 170–79. DOI: 10.1016/j.geomorph.2012.10.008. Heister, T. et al. 2017. Geophys. J. Int. 210 (2): 833–51. DOI: 10.1093/gji/ggx195. Hoggard, M. et al. 2020. Nature Geoscience 13 (7): 504–10. DOI: 10.1038/s41561-020-0593-2. Kronbichler, M. et al. 2012. Geophys. J. Int. 191 (1): 12–29. DOI: 10.1111/j.1365-246X.2012.05609.x. Neuharth, D. et al. 2022. Tectonics 41 (3): e2021TC007166. DOI: 10.1029/2021TC007166. Rodríguez, A. et al. 2021. GCubed 22 (6). DOI: 10.1029/2020GC009453.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".