Bibliographic record
Abstract
Modelling of magnetic rock properties from magnetic field observations has been an important practice in resource exploration for decades. However, the application of this practice has been limited by conventional thinking that assumes rock magnetization is dominated by induced magnetization such that magnetization direction is aligned with the geomagnetic field. Convention has also accepted that we are unable to model for magnetic remanence without a-priori knowledge of remanence direction and strength.Recent practical successes in directly modelling magnetization vector direction and strength using Magnetization Vector Inversion (MVI) have challenged these conventions, and MVI modelling is proving useful in practical exploration scenarios. The addition of new information, namely the direction and amplitude of magnetization, demands new thinking and approaches to understanding what this information means, and how to use the modelled direction of magnetization in practical situations.This paper presents a new statistical and quantitative approach to define and discriminate different magnetization domains within a full 3D MVI voxel model. Our studies show that modelled vector direction is meaningful even without prior knowledge of remanence (and other) magnetization characteristics. We also demonstrate that reasonable magnetization direction can be recovered from both weakly and strongly magnetized source rocks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".