Assessing target acquisition and tracking performance for complex moving targets in the presence of latency and jitter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main objective of our research is characterization of various motor profiles among primary school age children. We examined 89 Moscow schoolchildren of 8-10 years old, not involved in sports. We analyzed 25 indices from psychomotor, psychophysiologic, posturography and locomotor testing during our research. Children were divided in 4 groups according to its motor coordination character similarity by applying cluster analysis (k-means method). Thereby we obtained 4 clusters which, as we suppose, can be identified as different types of motor coordination or "individual motor profiles" of 8-10 years old children. The results of our research shows that there are no significant differences in age, gender, body composition within clusters, thus it can be used during sport-selection process. The most typical features of 1st cluster representatives are: the ability to anticipate and a high wrist speed; the 2nd cluster representatives are more successful in rapid, rhythmic, automatic actions, based on a mechanism of intrinsic programming. Representatives of the 3rd cluster are notable for a low level of postural control, smooth, accurate and rhythmic arm actions based on a visual feedback, and advanced sense of tempo and space sense. Representatives of the 4th cluster shows highest level of development of coordination ability, which are significant for them in reaching results in various hard-coordination sports and labor activities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it