Erratum to: Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data
Bibliographic record
Abstract
Correction 1: On page 9.There are two places to be corrected in the paragraph regarding Tables 5 and 6."Table 5 shows that the variance estimate of each level-2 random loading (λ j s) across the subjects and its standard error obtained from bootstrap samples.Here, the random loading denotes the deviation between the fixed loading and individual subject's estimate of the loading.Thus, the variance estimate of each level-2 random loading represents inter-subject variability in the loading.It was found that the variances of the random loadings for all observed variables turned out to be statistically significant.This suggests that there existed substantial subject-wise differences in each of the loadings.Table 6 exhibits the variance estimate of the Level-2 random path coefficient (θ i j s) of each latent variable and its standard error obtained from bootstrap samples.About 87% of the variance estimates of the random path coefficients turned out to be statistically significant, suggesting substantial differences in each of the path coefficients across the subjects."The parts indicated above should have read as follows:"Table 5 shows that the variance estimate of each level-2 random loading (λ j s) across the subjects and its standard error obtained from bootstrap samples.Here, the random loading denotes the deviation between the fixed loading and individual subject's estimate of the loading.Thus, the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.192 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.117 | 0.058 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".