A New Technique for Low Magnetic Latitude Transformation: Synthetic Model Results and Examples
Bibliographic record
Abstract
Traditional methods for processing low magnetic latitude data below ±25° magnetic latitude do not fully resolve anomaly location and may distort the anomaly shape leading to misinterpretation of the data, as well as loss of certain directional anomalies. The principal objective of our research was to develop a new filter that would better position anomalies, reduce anomaly distortion and provide good anomaly shape while attempting to recover structures parallel to the declination direction.The MTC-LML Filter (Modulus of Total Component at low magnetic latitudes) is based on the calculation of the Modulus of the three magnetic components of the main field (one vertical and two orthogonal horizontal). To test the MTC-LML Filter, a set of synthetic models were constructed composed of complicated magnetic bodies each with different strike directions and depths. Each model was designed to address a specific aspect of the low magnetic latitude problem. The results of synthetic modelling for the MTC-LML Filter were compared with synthetic models of standard transformations and techniques for dealing with low magnetic latitude data. Practical applications of the MTC-LML Filter were then made using survey data.The filter results for synthetic model and survey data, show better location and shape of model source bodies when compared to the existing standard transformations and techniques. The MTC-LML Filter provides an improved transformation of the data with reliable location and shape of the anomalies above the source, reduced anomaly distortion and better recovery of structure parallel to the declination direction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".