Apparel with Far Infrared Radiation for Decreasing an Athlete's Oxygen Consumption during Submaximal Exercise
Bibliographic record
Abstract
Far infrared radiation (FIR) has been shown to have physiological effects when used as a treatment modality for certain medical conditions. Athletic apparel are currently commercially available that are constructed with fabrics that purportedly emit FIR. If apparel with this technology are capable of inducing positive physiological effects, then there may be important implications when worn by an athlete during exercise. The purpose of this study is to examine whether FIR apparel has an effect on oxygen consumption during exercise at submaximal intensities. Twelve male cyclists have completed submaximal incremental cycling tests. Each subject is tested on 4 separate days, twice while wearing a full body Control garment, and twice while wearing a similar garment made out of FIR fabric. Throughout each cycling test, the volume of oxygen uptake is monitored by using a breathing mask and metabolic analysis cart. At lower cycling intensities, the subjects consume statistically significantly less oxygen when wearing the FIR apparel compared to the Control garment, despite performing the same amount of mechanical work. Additional research is required to determine the implication of this effect for a training or competing athlete; however, the results indicate that this apparel technology does elicit a physiological effect.
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".