Integrated 3D modelling and associated machine learning targeting: the Jaguar Greenstone Belt example.
Bibliographic record
Abstract
Mira Geoscience completed an integrated interpretation in the Jaguar Greenstone Belt (JGB), in Western Australia, on behalf of Round Oak Minerals (now Aeris Resources). The 3D structural and stratigraphic regional model, consistent with geophysical data sets was the foundation for the exploration model. The targeting and prospectivity analysis were based on quantifying exploration criteria and explicitly representing these criteria in the exploration model for sub-seafloor replacement-style Volcanic Hosted Massive Sulfide (VHMS) deposits. First, the regional geological model was built from geological constraints (mapping, drill holes) but also developed in close integration with potential fields data, producing a viable starting model for geologically constrained inversion to solve for rock property variations within geological domains. When the model thus constructed was submitted to geologically constrained inversion to reconcile unexplained response as property variations within those domains, sensible/stable property variations were recovered in the inverted model, which it was possible to interpret in terms of alteration and potential targets. The exploration criteria were translated using the integrated 3D model to create exploration vectors that were representative of the mineral system. In other words, these vectors were numerical realisations of the various targeting criteria. The prospectivity analysis at Jaguar used a Machine Leaning approach, namely Random Forests, to generate a 3D Mineral Potential Index based on different combinations of input exploration vectors. This resulted in identification of 41 separate targets within the JGB.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.037 |
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; both teacher heads agree on what is shown here.
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".