Comparison of three different methods to select feature for discriminating forest cover types using SAR imagery
Why this work is in the frame
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Bibliographic record
Abstract
Three methods (fuzzy partition method, stepwise regression analysis and principal component analysis) were used to select meaningful texture features for discriminating forest cover types. The initial texture set was extracted from the wavelet sub-images. Feature selection was based on all texture features of four sub-images combined. Recognition of forest cover types was accomplished by the neural network of learning vector quantization. The performance of these techniques was evaluated using a case study area at Whitecourt, Alberta, Canada. The selection procedure seemed to be adequate to extract meaningful texture features to help discriminate forest cover types, because the classification accuracy of the selected feature sets was improved. In addition, the optimization process can be considered as an efficient one, since the number of features was reduced to about 24.5-66.8% of the total 208 features using the three selection methods.
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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