Shifted factor analysis—Part III: <i>N</i>‐way generalization and application
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The ‘quasi‐ALS’ algorithm for shifted factor estimation is generalized to three‐way and n ‐way models. We consider the case in which mode A is the only shifted sequential mode, mode B determines shifts, and modes above B simply reweight the factors. The algorithm is studied using error‐free and fallible synthetic data. In addition, a four‐way chromatographic data set previously analyzed by Bro et al. ( J. Chemometrics 1999; 13: 295–309) is reanalyzed and (two or) three out of four factors are recovered. The reason for the incomplete success may be factor shape changes combined with the lack of distinct shift patterns for two of the factors. The shifted factor model is compared with Parafac2 from both theoretical and practical points of view. Copyright © 2003 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 | 0.003 |
| 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