Corrigendum to: “FreeSurfer subcortical normative data” [Data in Brief 9 (2016) 732–736]
Bibliographic record
Abstract
We have recently uncovered a flaw in our statistical method that impacts the semi-partial R 2 results presented in Figure 1.These values were computed using the option Effect size within the GLM procedure of the SAS statistical software and are labeled by SAS as semi-partial eta squares.These values are, in fact, a partitioned R 2 according to a given order of predictors' entry.The main impact of this procedure is that the order of the predictors influences the semi-partial R 2 .While it does not impact the regression models, for those readers that are drawing conclusions based on the relative importance of the predictors, we felt compelled to provide more accurate and robust results.In all of our analyses, the predictors were listed in a given, unvarying order as presented in the article tables (age, age 2 , age 3 , sex, estimated intracranial volume (eTIV), eTIV 2 , eTIV 3 , magnetic field strength, GE manufacturer, and Philips manufacturer, followed by interactions).Therefore, the variables listed earlier were favored in terms of R 2 compared to the variables entered later.This new Table 1 shows R 2 for each predictor computed using the calc.relimpfunction of the R package relaimpo (relative importance in linear models).The metric used is lmg, based on Lindeman, Merenda, and Gold (1980), which is a R 2 partitioned by averaging sequential sums of squares over all orderings of the predictors, effectively correcting this situation.While the total R 2 remains intact, the main difference of this new metric compare to the original is that for nearly all regional volume, age and sex have lower R 2 (mean age: -7% (range: -12 to 0), sex: -4% (-10 to 0)) while
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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 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.359 | 0.205 |
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".