Modular organization of the murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles
Bibliographic record
Abstract
In this study, we made use of non-negative matrix factorization (NMF) to extract muscle synergies from electromyographic (EMG) data. We implemented the NMF algorithm in R version 3.5.1 (R Foundation for Statistical Computing, R Core Team, Vienna, Austria), a programming language available in a free software environment. However, even if the software does not require a paid license, often researchers are either not confident with or prefer not to spend time writing the code required to perform NMF. We make available, as we recently did with human data (Santuz <em>et al.</em>, 2018), an example open access data set of EMG and muscle synergy data for murine walking and swimming. The data presented in this supplementary information part is available in three formats: 1) the raw EMG of two example trials (one recorded during walking and the other during swimming in a wild type animal, six muscles), unprocessed together with the touchdown and lift-off timings of the recorded limb for walking and the cycle timings for swimming; 2) the filtered and time-normalized EMG and 3) the muscle synergies extracted via NMF. Moreover, we provide the R code for obtaining the results described in the previous three points. We do not report any metadata, since trials are relative to a single representative animal. The R code is profusely commented.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".