A comparison of models of force production during stimulated isometric ankle dorsiflexion in humans
Bibliographic record
Abstract
In this paper, we compare seven models on their ability to fit isometric muscle force. We stimulated the ankle dorsiflexors of eight subjects at seven ankle angles (85 degrees-120 degrees). Three different stimulation patterns (twitch, triangular, and random) were applied at all ankle angles. Four additional patterns (doublets, steady rates, "catch property," and walking-like) were applied at 95 degrees. Parameter values were optimized for each model at each angle. Parameters for the general linear model were calculated using a novel least-squares algorithm. A linear, second-order critically damped model gave the poorest fits (average root mean square (rms) error: 15 N). The models of Ding et al. (2002) and Bobet and Stein (1998) gave the best fits (average rms errors: 9.2 and 9.4 N). The other models (general linear second-order model, Wiener model, Zhou et al. (1995) model, general linear model) gave intermediate results. Results were similar at all ankle angles. We conclude that the Ding and Bobet-Stein models are the best overall for isometric contractions, that no linear model of any kind will give an error less than 9% of maximum force, and that the models tested are consistent across lengths.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".