Longitudinal Changes in Body Composition Throughout Successive Seasonal Phases Among Canadian University Football Players
Bibliographic record
Abstract
Kim, J, Delisle-Houde, P, Reid, RER, and Andersen, RE. Longitudinal changes in body composition throughout successive seasonal phases among Canadian university football players. J Strength Cond Res 32(8): 2284-2293, 2018-The purpose of this study was to assess changes in body composition during seasonal phases of the training year among Canadian Inter-University Sport (CIS) football players. Forty university football players were assessed for anthropometry, total body composition, regional body composition, and central adiposity over a 7-month period including the summer off-season and the in-season. Baseline testing occurred in April, before the summer off-season, and follow-ups were completed before training camp, at the beginning of August, and following the in-season, at the beginning of November. Linemen had the greatest tissue percent fat (25.98 ± 6.56%) at baseline, significantly (p < 0.01) greater than big skill (18.69 ± 3.97%) and followed by skill (14.35 ± 3.39%) who were significantly (p < 0.01) leaner than both other groups. Skill players significantly increased fat mass (0.98 ± 0.30 kg, p ≤ 0.05) and waist-to-hip ratio (0.02 ± 0.01, p ≤ 0.05) during the in-season, and linemen increased visceral fat mass from April to November (0.20 ± 0.06 kg, p ≤ 0.01). All players significantly (-1.26 ± 0.30 kg, p = 0.001) decreased lean mass during the in-season. All groups significantly increased bone mineral content during the summer off-season (p ≤ 0.05). There was also a significant time × summer training location interaction (p ≤ 0.05) for fat mass with athletes who remained on campus during summer months gaining the least amount of adiposity. Body composition and central adiposity seem to change differentially among positional groups across the annual training season.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".