Experimental strategies for identifying suitable mechanical models of the head-neck system
Bibliographic record
Abstract
Musculoskeletal systems in humans and other vertebrates can be difficult to model realistically because their many elements have diverse and specialized properties. Computer graphical tools are now available that can organize the large numbers of parameters needed to quantify articular relationships and muscle attributes in a particular motor system. However, such models may be overly complex for the general use of scientists and engineers whose interests are directed at the control principles underlying particular movements. Thus, approaches must be developed to simplify the detailed models in ways that capture the relevant features of the system but reduce the level of morphological and computational complexity. The authors are using three methods to simplify models of the feline head-neck system that can be used to describe visually guided head movements in the horizontal and vertical planes. 1. Videofluoroscopy is used to identify patterns of joint motion that occur during head movements in particular planes. 2. Electromyographic recordings are used to identify which of the more than two dozen neck muscles are active during the same head movements. 3. Anatomical dissections and geometric representations are used to gain insight into the torque-generating capabilities of muscles in different head-neck postures. These approaches taken together have helped to identify structures that might be collapsed or eliminated in simpler models to describe these movements. They have also helped to identify deficiencies in the more complex graphical models that are currently under development.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".