SFE2D: A Hybrid Tool for Spatial and Spectral Feature Extraction
Bibliographic record
Abstract
A crucial task for integrated geoscientific image (geo-image) interpretation is the relevant geological representation of multiple geo-images, which demands high-dimensional techniques for extracting latent geological features from high-dimensional geo-images. A standalone mathematical tool called SFE2D (spatiospectral feature extraction in two-dimension) is developed based on independent component analysis (ICA), continuous wavelet transform (CWT), k-means clustering segmentation, and RGB color processing that iteratively separates, extracts, clusters, and visualizes the highly correlated and overlapped geological features from multiple sources of geo-images. The SFE2D offers spatial feature extraction and wavelet-based spectral feature extraction for further extraction of frequency-dependent features. We show that the SFE2D is a robust tool for automated pattern recognition, fast pseudo-geological mapping, and detection of regions of interest with a wide range of applications in different scales, from regional geophysical surveys to the interpretation of microscopic images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".