An investigation of orthogonal signal correction algorithms and their characteristics
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Bibliographic record
Abstract
Abstract Six different algorithms for orthogonal signal correction (OSC) are studied and compared both from an algorithmic point of view and from a prediction and analysis point of view. The algorithms have appeared under the names OSC (three alternative algorithms), direct orthogonalization (DO) and orthogonal projection to latent structures (OPLS). These algorithms can be divided into two groups. The first group has the ability to reduce the number of PLS components in the calibration models significantly by removing only one orthogonal component. The second group reduces the complexity of the calibration model by one PLS component for each orthogonal component removed. The methods are evaluated and compared using both simulated and real calibration data sets. In some cases the OSC algorithms can have quite different behaviors, such as when non‐linearities are present. However, in all cases we have studied, none of the OSC algorithms provided a significant improvement in the calibration models over using PLS on the raw data. The main advantage with OSC may lie in the possibly easier interpretation and understanding from the analysis of corrected data. Analysis of the orthogonal information removed with OSC might also be beneficial. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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