Regionalisation of the muscle fascicle architecture in the equine <i>longissimus dorsi</i> muscle
Bibliographic record
Abstract
REASON FOR PERFORMING STUDY: The longissimus dorsi is the largest muscle in the equine back and plays an important role in locomotor ability and performance in the horse. In vivo studies suggest that the mechanical function varies between different muscle segments, in part determined by anatomy. It is possible therefore that variations in function reflect variations in the anatomy of the longissimus dorsi along its length. OBJECTIVES: To identify if there are regional variations in muscle architecture of the longissimus dorsi. METHODS: Computed tomography scans were obtained from 8 cadaver backs to identify the cross-sectional area and volume of the muscle. The 3D orientations of the muscle fascicles were quantified in situ by their direction cosines using a magnetic digitisation system. RESULTS: Mean +/- s.e. volume of the muscle was 2.34 +/- 0.18 l for each side and mean length of the vertebral column from T4 to L6 dorsal spinous processes was 56.57 +/- 2.0 cm. There were significant differences in the cross-sectional area and the moment arm lengths between muscle segments (P<0.05). In the lumbar region, the mediolateral moment arms were 2-3 times greater than the dorsoventral moment arms. There were significant variations in the direction cosines (n = 7129) between muscle segments and between different regions within each segment (P<0.05). POTENTIAL RELEVANCE: These observations indicate that the muscle fascicle architecture predisposes the longissimus dorsi to different functions both along its length and between different regions within each segment. Detailed 3D anatomical measures of the structure with in vivo measures of function (back motion and muscle activity) will be investigated in further studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".