Iterative spatial filtering for reducing intra-class spectral variability and noise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intra-class variability and noise are obstacles that obscure subtle differences between spectral classes in hyperspectral imagery. This paper presents an iterative adaptive smoothing filter (IAS), which considers inherent spatial characteristics of image classes and the assumed random nature of pixel to pixel noise to minimize intra-class variability and noise. IAS makes use of standard hyperspectral spectral similarity measures, spectral angle and root-mean-squared error, to calculate and apply weighting functions to filter image pixels. Using a small window assures that spatially independent classes with subtle spectral differences can still be distinguished. The result is a change in the internal density distribution of the data volume (intra-class variability and noise), but the overall volume undergoes little change (inter-class variability). The usefulness of the filter is illustrated with simulated and real hyperspectral data.
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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 it