A Theoretical Training Plan and Facility for Ontario-Based Olympic Weightlifters
Bibliographic record
Abstract
Olympic Weightlifting is a sport where athletes of different weight classes utilize either the Snatch or Clean and Jerk (CJ) technique to lift the heaviest load overhead. This case study describes the training program for a team of Senior (age; 18-35 years) Olympic weightlifters during a normal competitive season. The team of Olympic weightlifters will be participating in team training 3 to 4 times a week; however, variations will occur on the training schedule for each individual athlete based on the on-going testing results. Each athlete will also participate in an additional day of training noted as the “active rest” day which prioritizes injury management for the individual based on their test results, during the training season. In conclusion, the training program in this study provided an insight on implementing velocity profiling into an Olympic weightlifting training program. The device in this study (PUSH Band) quantifies the bar movement via the force velocity curve and the coach has a clearer means in which to determine the repetitions that follow. This study is based around the theoretical idea of implementing a means in which to avoid the traditional periodization rep schema to administer training volume, but rather utilize new technologies. To conclude, methods that accommodate for velocity profiles to determine volume in conjunction with 1RM percentages and a coach’s discretion may allow for more efficient training and a reduction in excess fatigue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.010 |
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".