Unearthing Australia’s subsurface secrets - An integrated FAIR modelling approach
Bibliographic record
Abstract
<strong>Output Type: </strong>Exploring for the Future Extended Abstract<br><strong>Short Abstract: </strong>To enable a sustainable and responsible use of the Earth's subsurface environment and accelerate Australia’s energy transition to net zero, industry and government rely on a quantified knowledge of Australia’s geology and structure to inform their decision-making. Despite the wealth of subsurface data available across the continent, including stratigraphic boreholes, geological and geophysical data, their uneven distribution, variable quality and diverse data formats prevent the development of consistent national subsurface models of Australia’s geology. A national coordination of data compilation with common standards, along with development of open-source modelling tools in line with the Findable, Accessible, Interoperable and Reusable (FAIR) principles are required to address this challenge. Here we present an open-source modelling methodology which provides a national seamless chronostratigraphic framework to assess the depth and spatial extent of geological and geophysical horizons to inform decisions on resource exploration and development. Using this approach, regional subsurface models across ~26% of Australia were generated, characterising depth and thickness of key stratigraphic sequences, i.e. Cenozoic, Mesozoic, Paleozoic and Neoproterozoic. Combined with the layered geology map of Australia, this work supports the basis for an integrated geological framework that facilitates data-driven decision making for decarbonation strategies, land-use management, exploration strategies and water management. <br><strong>Citation: </strong>Bonnardot, M.A., Grose, L., Wilford, J., Du, P., Hope, J., Wong, S.C.T., Vizy, J. & Rollet, N., (in prep). Unearthing Australia’s subsurface secrets - An integrated FAIR modelling approach. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://doi.org/10.26186/149719
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".