Correction: Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing
Bibliographic record
Abstract
there is a few changes in the Additional file which was originally published with this article; it has now been replaced with the correct file.Then the values under the Methods and Results has been changed.So, the Methods and Results will read as follows:Methods: We assessed participants twice within 15 days on all tasks presently available in KST.We determined the 5-95% confidence intervals for each task parameter, and derived thresholds for significant change.We tested for learning effects and corrected for the false discovery rate (FDR) to identify task parameters with significant learning effects.Finally, we calculated intraclass correlation of type ICC (3,1) (ICC-C) to quantify consistency across assessments.Results: We recruited an average of 56 participants per task.Confidence intervals for Z-Task Scores ranged between 0.84 and 1.41, and the threshold for significant change ranged between 1.19 and 2.00.We determined that 6/11 tasks displayed learning effects that were significant after FDR correction; these 6 tasks primarily tested
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".