Cluster analysis of Euler deconvolution solutions: New filtering techniques and geologic strike determination
Bibliographic record
Abstract
Abstract Euler deconvolution often presents the problem of filtering coherent solutions from uncorrelated ones. We have applied clustering and kernel density distribution techniques to a Euler-generated data set. First a kernel density distribution algorithm filters uncorrelated Euler solutions from those consistently located near an anomalous magnetic-gravimetric source. Then a fuzzy c-means clustering algorithm is applied to the filtered data set. The computation of cluster centers reduces the size of the data set considerably, yet maintains its statistical consistency. Finally, the computation of eigenvectors and eigenvalues on the cluster centers yields an estimate of the geologic strike of the anomalous sources responsible for the observed geophysical anomalies. Therefore, we can obtain an improved strike and depth estimation of the magnetic sources. Although the algorithm can filter and cluster any Euler data set, we recommend obtaining the best solutions possible before any clustering. Hence, we have used a hybrid 3D extended Euler and 3D Werner deconvolution algorithm. We have developed synthetic and real examples from the Bathurst Mining Camp (New Brunswick, Canada). The output of this algorithm can be used as an input to any 3D geologic-modeling package.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".