Evidence for Functional Partitioning of the Rabbit Digastric Muscle
Bibliographic record
Abstract
The rabbit digastric muscle has a single belly that opens and retracts the mandible. It does not contain connective tissue partitions, and all fibers arise from the same tendon and insert into a single broad site. Historically, it was assumed that the muscle functioned as a single unit. Since we had preliminary evidence that this might not be the case, we carried out five small studies in rabbits. First, we showed that electromyographic (EMG) activity varies between recording sites within the muscle during the masticatory cycle induced by repetitive stimulation of the sensorimotor cortex. We found that EMG activity in the caudal region sometimes began before the anterior EMG during mastication when the jaw swung to the side of the muscle, but the two regions became active at the same time during other patterns. We next showed that separate branches of the mylohyoid nerve enter the anterior, intermediate and caudal regions of the digastric. However, a separate study showed that the motor endplates were distributed across a continuous sheet, consistent with a single anatomical partition. We then stimulated single nerve branches to deplete glycogen. By comparing the optical density of fibers labeled by the periodic acid-Schiff method for glycogen, we were able to show that the three branches innervate separate regions of the muscle. Finally, we applied either FluoroGold or Fast Blue dyes to the central cut ends of the branches to label the cell bodies of the three pools of motoneurons. These were found within the middle and caudal thirds of the trigeminal motor nucleus, but there appeared to be no spatial separation of the three pools or double labeling of cells. We conclude that the digastric muscle contains two and possibly three functional subregions. The fact that the motoneurons are intermingled suggests that the distribution of motor commands to the three pools is not based on their location.
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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 | 0.000 | 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 | 0.001 |
| 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".