Integrating geological constraints in geophysical models
Bibliographic record
Abstract
There are three possible schemes by which one can compute geophysical models: discrete body, lithologic surface, and voxel mesh inversion. Each of these model schemes employs three attributes of the anomalous source body: location, geometry, and physical property contrast. The different computational approach employed by the three methods relating to source body geometry causes emphasis to be placed on either the geological or the geophysical data. The discrete body and lithologic surface methods are controlled by prior geological knowledge of the source geometry. In contrast the unconstrained voxel inversion method is driven by the geophysical data. The interpreter is required to decide which of these model schemes is appropriate to each specific problem. For example, when looking at diabase dike geometry in the Sudbury Basin the discrete body method must be used, When attempting regional geological mapping of the Baie Verte Peninsula the lithologic surface method is more practical. Finally, when attempting to model a complex fold structure a constrained inversion approach is most appropriate. Eventually it is anticipated that fully constrained inversions will actually incorporate elements of all three modeling schemes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".