Metal Earth: Role of multidisciplinary geophysical methods to improve knowledge of mineral deposition across Precambrian rocks
Bibliographic record
Abstract
SummaryRecently, there was an increase of costs to explore new economical mineral occurrences, while the success of discovery of new deposits has diminished. Therefore, Metal Earth was conceived with the objective to improve understanding of the processes controlling the distribution of mineral endowment, with a focus on Archean greenstone belts of the Canadian Superior Province as a global representative of these belts. Part of Metal Earth is to study thirteen different transects with a total length of >1000 km associated with Archean greenstone belts with various degrees of mineralisation (from less endowed to well-endowed) using high-resolution multidisciplinary geological and geophysical data. The goal is to reliably identify deeper underlying crustal/mantle conditions controlling mineralisation and also to validate existing ore deposit models by better identifying components contributing to mineral endowment.In this paper, we discuss acquiring, processing and preliminary modelling of multidisciplinary geophysical data (i.e. seismic, magnetotelluric, potential field data and petrophysical characterisations) to reliably model subsurface features along the transects. Across the Swayze transect, seismic sections clearly show sub-horizontal and dipping geological boundaries and magnetotelluric model reveals pathways as narrow upper crustal sub-vertical zones of low resistivity. In addition, joint inversion of gravity and magnetic data on initial models constrained by surface geology, seismic and magnetotelluric models and petrophysical characterisations have improved the models by better detecting at depth the limits of lithological units, low-density batholiths, dykes and fault zones.New models reveal there are significant crustal difference between the well-endowed Abitibi and the less prospective Wabigoon subprovinces. The final models will map the lithospheric architecture and constrain large-scale intracratonic controls on magmatism, crustal and tectonic evolution, and subsequently mineralisation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".