Calculation of muscles forces in Labrador retriever hind legs during stance
Bibliographic record
Abstract
What scaled canine muscle parameters can tell about their functional properties.Reasons for performing study: As part of a musculoskeletal modeling effort, muscle parameters from the canine hind limb are collected, including fiber length and physical cross-sectional area (PCSA).Objectives: To examine a ratio of PCSA to fiber length to evaluate muscle function.Study Design: This study reports a data set of optimal fiber lengths and muscle volumes acquired in 6 canine hind limbs, and examines the use of a PCSA/fiber length ratio in discerning muscle function.Methods: 24 muscles were dissected from the right hind limb in six dogs weighing between 28 and 52 kg, weighted and treated with 10% formaldehyde solution for 48-72h, 0.4M phosphate-buffered saline solution (pH 7.2) for 24-48h and 20% sulphuric acid solution for 3-7 days.Once the muscle fibers were loosened, depending on the size of the muscle, 3-8 individual muscle fibers were removed from different muscle sites to ensure a representative sample.Muscle fibers were then measured based on photographs taken on grid paper.Fiber length was normalized to limb length (L
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".