Adopting multispectral dip components for coherence and curvature attribute computations
Bibliographic record
Abstract
Abstract Although volumetric coherence is the most widely used geometric attribute, accurate estimates of volumetric dip are in some ways more important. Coherence, amplitude gradients, and gray-level co-occurrence matrix textures should be computed along structural dip. Curvature and aberrancy are computed from volumetric estimates of structural dip. Because of both differences in resolution and sensitivity to coherent noise, different frequency components may exhibit different dip. In recent years, improvements in coherence have been noticed where covariance matrices of individual spectral components are summed rather than summing the original broadband data. We extend the same concepts to compute multispectral dip estimates by using a gradient structure tensor algorithm. The results are sharper, less smeared images on the dip components. The higher-resolution dip estimates result in higher-resolution curvature and aberrancy estimates. Availability of sharper estimates of dip to guide coherence attribute results in more continuous, less noisy discontinuities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".