Locating skarns with magnetic survey data, Geyer, Erzgebirge: optimizing data acquisition procedures
Bibliographic record
Abstract
ABSTRACT Magnetic data can be acquired from a number of different platforms (e.g., ground, drone, helicopter) using a variety of sensors (e.g., caesium vapour‐type optically pumped magnetometers, fluxgate, superconducting quantum interference devices) with different flight line configurations. To detect a magnetic anomaly associated with a mineral commodity that is not exposed but is thought to be associated with the anomalous magnetic mineral content, it is necessary to optimize the survey parameters through a complete data integration process. Prior petrophysical measurements provide insight into the physical contrast that might be expected between adjacent lithologic units and between the ore zone and the encompassing lithology. Oriented rock samples provide access to magnetic remanence data through palaeomagnetic laboratory measurements. Knowing the typical morphology of the ore zone one can compute a forward model of the expected anomalous response and determine which combination of survey parameters provides the highest probability of detecting the commodity being sought. In this study, we analyse magnetic patterns associated with thin dipping skarn bodies from the Geyer mining district in Erzgebirge, Germany. Petrophysical measurements indicate that the skarns are more magnetic than the surrounding host rock. Partially oriented samples from a bore core record a Variscan age metamorphic remanence. Forward modelling indicates that clusters of skarn bodies are required to produce a reliably detectable magnetic signal. Ground, or low elevation drone surveys are needed to detect these anomalies with standard scalar‐type optically pumped magnetometer or fluxgate magnetic surveys. The enhanced spatial resolution and long‐wavelength rejection of a superconducting quantum interference device based full‐tensor magnetic gradiometer provide an improvement over optically pumped magnetometers for an aircraft‐based survey platform.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".