First national mineral system assessment of Australia's iron oxide copper-gold potential
Bibliographic record
Abstract
<strong>Output type:</strong> Exploring for the Future Extended Abstract<br><strong>Short abstract: </strong>Iron oxide copper-gold (IOCG) deposits are a significant source of copper and gold and can also contain critical minerals that are required for the transition to a low carbon economy and to increase Australia’s security of mineral supply. Given their strategic importance, a national-scale assessment of the mineral potential for IOCG mineral systems in Australia has been undertaken using a hybrid data- and knowledge-driven approach. The national-scale assessment includes the evaluation of the statistical importance of mappable criteria used in previously published regional-scale IOCG models, resulting in the removal of five criteria and the inclusion of four new or revised criteria derived from datasets developed through the Exploring for the Future program. The new mineral potential model successfully predicts the location of 91.7% of known IOCG deposits and occurrences in 8.3% of the area, reducing the exploration search space by 91.7% and highlighting new areas of elevated prospectivity in under-explored regions of Australia. When compared to existing regional-scale mineral potential assessments for IOCG mineral systems published by Geoscience Australia, the new national-scale model demonstrates higher prospectivity in areas with known IOCG deposits and occurrences, while also highlighting new prospective areas for IOCG mineral systems. Areas with assessed high prospectivity but lacking known IOCG mineralisation include parts of the Curnamona, Etheridge and Musgrave provinces, and the Delamerian, Halls Creek and Tanami orogens. <strong>Citation</strong>: Cloutier J., et al., 2024. First national mineral system assessment of Australia's iron oxide copper-gold potential. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://doi.org/10.26186/149357
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".