Automated identification of lava flow structures using local Fourier spectrum of digital elevation data
Bibliographic record
Abstract
The rheological parameters of a lava flow can be inferred by studying structural features on the solidified lava surface. We have developed a technique using the S‐transform (a localized Fourier transform) to analyze and interpret digital elevation data. We examined structural features of a silicic lava flow, the Medicine Lake dacite flow in California. The S‐transform accurately identified and located both repeating and single‐instance structures. Several classes of structures were identified in the data, including the following: localized short wavelength features (1.3–3.6 m in wavelength) identified as large blocks and repeating medium wavelength features (5–8, 10–16, 18–26, 30–36, and 56–67 m in wavelength) identified as multiple generations of crustal folding. Fold wavelengths determined by the S‐transform and a model of compressional folding associated with near surface viscosity gradients were used to evaluate the rheology of the Medicine Lake dacite flow. The similarity of the amount of strain during folding events and the wavelength ratio of successive generations of folding suggest that the flow rheology remained relatively constant during folding of the flow surface. Repeating long wavelength features (67–70‐ and 105–140‐m wavelengths) were identified as the spacing of crease structures. Crease structures were recognized by the absence of short wavelength features at the location of their smooth flanks and by a central spike corresponding to the central trough. The limitations and applicability of this approach lie in the accuracy and resolution of the digital elevation data used.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".