Is There Any Non-functional Training? A Conceptual Review
Bibliographic record
Abstract
(FT) is a different approach from traditional strength, power, flexibility, and endurance (aerobic or cardiorespiratory) training already adopted in the physical training plan of professional, recreational athletes, healthy, and older adults. The 20 most recent papers published involving FT were searched in the PubMed/Medline database. Definition, concepts, benefits, and the exercises employed in FT programs were analyzed. The main results were: (a) there is no agreement about a universal definition for FT; (b) FT programs aim at developing the same benefits already induced by traditional training programs; (c) exercises employed are also the same. The inability to define FT makes the differentiation from traditional training programs difficult. Physical training programs can be easily described and classified as strength, power, flexibility, endurance, and the specific exercises employed (e.g., traditional resistance training, ballistic exercises, plyometrics and Olympic-style weightlifting, continuous and high-intensity interval training). This apt description and classification may provide consistent and clear communication between students, coaches, athletes, and sports scientists. Based on the current evidence and to avoid confusion and misconceptions, we recommend that the terms FT, high-intensity FT, and functional fitness training no longer describe any physical training program.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".