Z-Score distance: A spectral matching technique for automatic class labelling in unsupervised classification
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
The paper presents a post-classification tool that automatically labels classes in classified imagery by matching their spectral characteristics to reference spectra. Unlike the Spectral Angle Mapper (SAM) and other spectral matching classifiers, it labels clusters of pixels rather than individual pixels. This new method can be used to label or re-label classes generated by any existing classifier, either supervised or unsupervised. In other words, it can be used in conjunction with existing classification approaches or as a part of an ensemble classifier. A Landsat 5 TM image of an agricultural area was used for performance assessment. The spectral signatures (reference spectra) were extracted from a hyperspectral Hyperion data set. The technique produced a map of higher accuracy (51%) in comparison to maps produced by manual class labeling (40% to 45% accuracy, depending on the analyst); it also outperformed the SAM classifier (39%), but underperformed in comparison to the Maximum Likelihood classification (53% to 63% depending on the analyst).
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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