Human interaction in geophysical inversion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Numerical inversion of geophysical data does not normally require user interaction apart from the selection of initial inversion parameters. However, such an inversion often returns a single solution based upon default parameters. While this solution will be geophysically correct, assuming convergence of the algorithm, it may not be the most geologically reasonable answer. It is necessary to incorporate human interaction in selecting inversion solutions. We do this through an automatic system that provides a user-directed search of the space of geophysical solutions. Rankings assigned to numerical inversion results guide a genetic algorithm in advancing towards a conceptual target. Our example uses resistivity and chargeability data from a pole-dipole induced polarisation survey collected during a mineral exploration program. We invert for specific geological features: steeply dipping conductive and/or chargeable bodies below a weathered near-surface layer, separation of chargeability targets in a spatial sense, and greatest depth of resolution of the inversion algorithm. The interactive system is an organised way to investigate the solution space for results that emphasise these geological possibilities.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it