Assessment of corticospinal excitability during synchronous and asynchronous arm \ncycling
Bibliographic record
Abstract
Arm cycling is a rhythmic locomotor output which has a wide range of use both in clinical and for \nresearch purposes. In most cases, asynchronous arm cycling mode is usually used. In recent \nresearches, there are direct and indirect evidences to show that arm cycling, like other forms of \nhuman locomotor output, is produced by supraspinal inputs, spinally located specialized set of \nneurons called the central pattern generators (CPGs) and somatosensory inputs. The excitability \nof the corticospinal tract during arm cycling has been investigated when there are changes in \ncadence and load, but none has investigated corticospinal excitability during asynchronous and \nsynchronous arm cycling. Given that corticospinal excitability has been shown to be task \ndependent, there is possibility that neural control mechanisms during asynchronous arm cycling \nmight not be the same during synchronous arm cycling. Also, previous experimental researches \ndone in rhythmic non-locomotor output have hinted that rhythmic movement might be biased \ntowards the synchronous mode as cadence or frequency of movement increases. Hence, the \nprimary aim of this research is to investigate changes in corticospinal excitability during \nasynchronous and synchronous arm cycling at different cycling cadences.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".