Be as Upright as Possible When Squatting: Reply
Bibliographic record
Abstract
IN REPLY: Thank you for allowing us to respond to the Letter to the Editor highlighting our recently published article entitled, “The back squat: A proposed assessment of functional deficits and technical factors that limit performance. Strength Cond J 36: 4–27, 2014” (1). We thank Dr. Dan Cleather for his eagerness in emphasizing the importance of our commentary and continued discussions relative to its practical applications for practitioners. We agree with Dr. Cleather's contention that individual anthropometric variation in segments' length will influence technical focus in individuals, and thus, their optimal squat technique may vary with trunk lean and other coaching criterion discussed. We note that the target audience of our article are expert practitioners but rather coaches, educators, and parents who may lack depth of experience to teach technical performance of the squat. To capture this generalizability, our commentary discussed the “average” individual to allow for a more generic coaching cue that is to be adjusted to suit the individual. We remain encouraged by the stimulating ongoing discussions in the field to optimize coaching strategies for squatting performance. We thank Dr. Cleather for highlighting the relation of human anatomical variation to cueing, and we look forward to continued discussions in the shared pursuit of clinically meaningful solutions to optimizing squat performance in athletes of any age and size.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.048 | 0.059 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".