Analysis of Cognitive and Motor Functioning during Pubertal Development: A New Approach.
Bibliographic record
Abstract
We investigated cognitive-motor abilities in 303 (156 female) school children from Zagreb, Croatia, in the age span 10 to 14 years using a newly developed chronometrical reactionmeter system (CRD). The following tests were applied: CRD-311 (simple visual discrimination of signal location), CRD-324 (short-term memory actualisation), CRD-21 (simple convergent visual orientation), and CRD-11 (arithmetically conceptualised/operationalised convergent thinking). In both gender a statistically significant age related improvement of the performance for time related parameters (minimum time of test item solving (MT), total ballast (TB), and total time of test solving (TT) was observed. In contrast, the number of errors (NE), which was the only non-time related parameter tested, did not significantly change with age. Significant differences between boys and girls were observed for the time related parameters TB and MT. TB was significantly lower in girls, whereas boys tended to be faster in MT measurements. In TT as a composed measure of the mentioned parameters, no major differences were observed. We conclude that the CRD system is a new useful tool for investigating the complexity of cognitive-motor abilities in children. Our cross-sectional study demonstrated that the time-related parameters were significantly affected by age and gender during puberty.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".