Next generation 3D geological and geophysical modelling, west Tasmania
Bibliographic record
Abstract
Airborne magnetic and ground gravity data have been inverted to recover 3D distributions of magnetic susceptibility and density over an area (40 km x 35 km) in western Tasmania. Building on previous modelling and structural interpretation, the overall modelling strategy focused initially on the most magnetic units (Cambrian ultramafics), which are regionally associated with major tectonic features. Interpretation of their geometry via iterative 3D magnetic inversion and geometry adjustment thus outlines a structural framework capable of satisfying geologic and magnetic and gravity observations. Significant slabs of ultramafic material are demonstrated to underlie a considerable portion of the area, agreeing with previous 2D forward model investigations. This framework is effectively filled in with other major geological elements, including extensive low-density Devonian granites and magnetic Cambrian granites, such that geologic, magnetic and gravity constraints are all grossly satisfied. Magnetic and gravity residuals present at the conclusion of the modelling process are thus interpreted to indicate features such as alteration systems that are geologically as well as geophysically anomalous, and hence of exploration interest.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".