Application of Near Infrared Spectroscopy to Exercise Sports Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past 15 years the use of near infrared spectroscopy in exercise and sports science has increased exponentially. The majority of these studies have used this noninvasive technique to provide information related to tissue metabolism during acute exercise. This has been undertaken to determine its utility as a suitable tool to provide new insights into the heterogeneity and regulation of local tissue metabolism, both in cerebral and skeletal muscle tissue. In the accompanying articles in this symposium, issues related to the principles, techniques, limitations (Ferrari et al., 2004), and reliability and validity of NIRS in both cerebral and skeletal muscle tissue (Bhaambhani, 2004), mostly during acute exercise, have been addressed and will not be discussed here. Instead, the present paper will focus specifically on the application of NIRS to exercise sports science, with an emphasis on how this technology has been applied to exercise training and sport, and how it can be used to design training programs for athletes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it