Use of the analytic signal to identify magnetic anomalies due to kimberlite pipes
Bibliographic record
Abstract
Abstract The magnetic signature of most kimberlite pipes is, at high magnetic latitudes, a circular anomaly. At lower magnetic latitudes, it becomes asymmetric; and at the magnetic equator, the anomaly is mostly negative. The shape of the anomaly is also influenced by the presence of remanent magnetization. For a vertical cylinder, the shape of the analytic signal of the magnetic field is nearly independent of field orientation and remanence and always results in a compact, almost circular anomaly. A simple pattern recognition technique, based on a first-order regression over a moving window, between the analytic signal of the observed magnetic field and the theoretical analytic signal of a magnetic vertical cylinder is an effective tool to identify potential targets. Results where the correlation coefficient between the analytic signal and the theoretical analytic signal within a moving window are above a certain threshold are retained, and additional criteria can later be used to refine the target selection. The method's practical utility is demonstrated by applying it to three different sites. The first is a well-documented area located at high magnetic latitude (Ontario, Canada), the second is at low magnetic latitude (West Africa), and the last one is an area where many pipes have a negative magnetization (Lac de Gras, Canada).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.000 |
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".