Mathematical improvements to maximum likelihood parallel factor analysis: experimental studies
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Bibliographic record
Abstract
Abstract In this paper, the application of a number of simplified algorithms for maximum likelihood parallel factor analysis (MLPARAFAC) to experimental data is explored. The algorithms, described in a companion paper, allow the incorporation of a variety of correlated error structures into the three‐way analysis. In this work, three experimental data sets involving fluorescence excitation‐emission spectra of synthetic three‐component mixtures of aromatic compounds are used to test these algorithms. Different experimental designs were employed for the acquisition of these data sets, resulting in measurement errors that were correlated in either two or three modes. A number of data‐analysis methods were applied to characterize the error structures of these data sets. In all cases, the introduction of statistically meaningful information translated to estimates of better quality than the conventional PARAFAC estimates of concentrations and spectra. The use of the algorithms that employ the error structure suggested by the analysis of the error covariance matrix yielded the best results for each data set. Copyright © 2005 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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