Bibliographic record
Abstract
Originally developed as a specific form of exhaustive intermittent training involving 6–8 × 20 s of supramaximal-intensity cycling exercises with 10 s of recovery for athletes, Tabata training has become universally recognized around the world. The purpose of this review article is to provide a perspective on Tabata training and discuss how this popular style of intermittent training has evolved and been applied over the last ∼30 years. The article will review the original motivation behind Tabata training with relevance to concepts such as maximal accumulated oxygen deficit and maximal oxygen uptake (V̇o2max) and discuss how Tabata training has been adapted to involve sport-specific training and cross training. Studies of Tabata training on physiological responses and adaptations in muscle, blood vessels, bone, and brain across different populations will be reviewed. Finally, research on how Tabata-style training was applied to counteract inactivity during the COVID19 pandemic will be discussed. Evolving from the study of athletes, Tabata training represents an example of how high-intensity intermittent/interval training can be adapted and applied in various settings to enhance performance and health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".