Adaptation of seismic skeletonization for other geoscience applications
Bibliographic record
Abstract
2-D grid skeletonization, modelled after seismic skeletonization, can be applied to any gridded data, including images commonly used in the geosciences. It affords a map of distinctly separable discontinuities together with a suite of attributes associated with each discontinuity. Such a map, with an accompanying catalogue of attributes, offers a broad scope for data analysis such as directional or any attribute-oriented filtering. The versatility of our method lies in the choice of the primitive feature set used for pattern recognition, and in the two-pass application of the detection process. Application of the method to different data sets, including images of drainage basins, potential field data, reflection seismic data and invertebrates provide examples of how attribute catalogues are analysed to extract additional data-dependent information such as directional, event length, pulse width and non-linearity characteristics.
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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 | 0.000 |
| 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".