Bibliographic record
Abstract
Geoscience Australia’s Exploring for the Future (EFTF) program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. <br>The Layered Geology of Australia 1:1 000 000 scale dataset (2024 edition) is a seamless national coverage of Australia’s surface and subsurface geology. Geology concealed under younger cover units are mapped by effectively removing the overlying stratigraphy (Liu et al., 2015). This dataset is a layered product and comprises five chronostratigraphic time slices: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic. As an example, the Mesozoic time slice (or layer) shows Mesozoic age geology that would be present if all Cenozoic units were removed. The Pre-Neoproterozoic time slice shows what would be visible if all Neoproterozoic, Paleozoic, Mesozoic, and Cenozoic units were removed. In general, a top down approach has been taken so that only the uppermost units for each era are shown. However, in areas of relative geological complexity and where that detail was available in the source data, some units may show overlap. In these instances, the units are ordered from oldest (base) to youngest (top). The Cenozoic time slice layer for the national dataset was extracted from Raymond et al., 2012. Surface Geology of Australia, 1:1 000 000 scale, 2012 edition. Geoscience Australia, Canberra, and retains the data schema of that dataset. For that layer’s metadata, refer to https://dx.doi.org/10.26186/74855.<br>Geological units are represented as polygon and line geometries and are attributed with information regarding stratigraphic nomenclature and hierarchy, age, and lithology. All stratigraphic information populated in the dataset is derived from the Australian Stratigraphic Units Database. The dataset also contains a layer for structural features such as faults and shears.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.287 |
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".