A validated approach for collecting fine-wire electromyographic recordings in four canine shoulder muscles during highly dynamic tasks
Bibliographic record
Abstract
This study investigated the feasibility of obtaining ultrasound-guided intramuscular fine-wire electromyographic (fEMG) recordings from four canine shoulder muscles during highly dynamic activities. Four cadaveric canines were utilised to confirm the appropriate anatomical landmarks and the use of real time ultrasound guidance for electrode placement for four shoulder muscles: Biceps Brachii (BB), Supraspinatus (SP), Infraspinatus (IF), and Triceps Brachii – Long Head (TBLH). Electromyographic activity of the left BB, S P, IF, and TBLH was then recorded in two research dogs while walking and trotting to refine the data collection procedures. Finally, the full experimental protocol was piloted with two client-owned, specially-trained agility dogs, confirming the feasibility of collecting fEMG recordings while performing dynamic, highly-specific agility-related tasks and verifying our EMG amplitude normalisation protocol to enable comparisons across muscles and performance tasks. We present specific guidelines regarding the placement of fEMG electrodes and data collection/normalisation procedures to enable investigations of muscle activation during dynamic 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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".