Quantifying Effects of Dataset Size, Data Variability, and Data Curvature on Modelling of Simulated Age-Related Motor Development Data
Bibliographic record
Abstract
Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, past models have not accounted for these differences. The objective of this research was to create an algorithm that accounts for variability in sample size and age, modifying the curvature and variability of the data to test the accuracy and repeatability. While increased sample size improves the model, data collection is often limited by funding or population interest in participation. The simulations provide models of variability relative to sample size of the typically developing population from 5 to 18 years. The algorithm was evaluated with a sample of two-hundred and eighty-eight children. Using these models based on varying sample size, one can have greater confidence when identifying motor deficits from outlying data. Future researchers can use the model accuracy and repeatability information from this work to assess confidence in their own models based on the dataset size, data variability, and data curvature.
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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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".