A MECANOGRAFIA COMO TÉCNICA NÃO-INVASIVA PARA O ESTUDO DA FUNÇÃO MUSCULAR
Bibliographic record
Abstract
A mecanomiografia (MMG) é uma técnica nao-invasiva que registra as vibrações ou sons produzidos pelo músculo esquelético ao se contrair. As primeiras observações da existência destas vibrações foi feita há mais de trezentos anos, mas limitações tecnológicas fizeram com que a MMG só recebesse atenção nas últimas décadas. A teoria mais aceita para explicar o mecanismo dessas vibrações é a de que elas são produzidas pela contração tetânica incompleta das unidades motoras. O sinal MMG fornece informações relativas aos padrões de ativação elétrica e ao comportamento mecânico do músculo. Essa técnica pode ser utilizada para estudar as propriedades mecânicas do sistema muscular, o controle motor, a fadiga muscular entre outras aplicações. Mecanomyography (MMG) is a non-invasive technique that records the vibrations or sounds produced by skeletal muscle during contraction. The first observations of the existence of these vibrations/sounds occurred more than three hundred years ago, but due to technological limitations the MMG only received attention in the last few decades. The most accepted theory to the mechanism of these vibrations is that they are produced by the unfused tetanic contraction of motor units. The MMG signal provides information related both to the activation patterns and to the mechanical behavior of skeletal muscle. This technique might be used to study the mechanical properties of the muscular system, motor control, muscle fatigue amongst other applications.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".