The acceptance of lentils as a pre‐meal in soccer‐specific intermittent exercise and its effects on performance
Bibliographic record
Abstract
Our objective was to examine the acceptance, and effects on metabolism and performance of lentils as a pre‐exercise meal in a simulated soccer match. Thirteen male athletes participated in 4 trials in a repeated crossover design. Along with a fasted control (C) condition, isocaloric low GI‐high protein [lentils (L)], high GI‐high protein [potato & egg whites (PE)], or high GI‐low protein [potato (P)] meals were consumed 2‐hr before a soccer match. Blood and expired gas samples were collected to assess macronutrient metabolism. Distance covered on 5×1‐min sprints (2.5‐min rest) performed at the end of the match assessed performance. A 5‐point Symptoms Rating Scale (SRS) was used to assess nausea, bloating, hunger, fullness and flatulence. Serum insulin at exercise initiation was higher in P than all other conditions (p<0.001). During exercise, the oxidation rates for carbohydrate was higher and for fat was lower in P compared to C (p<0.05). Initial distance covered was greater (sprints 1 & 2) for L and PE (sprint 2) than fasting. Ratings of perceived exertion (RPE) throughout exercise were lower in L compared to C and P (p<0.05). No differences in symptoms (SRS) were apparent among the fed conditions (p>0.05). Also, the % of meals consumed was similar (80, 81 & 78% for L, PE & P, respectively). Our results show improvements in initial repeated sprint performance after L and PE meals compared to C. This finding along with the L meal's low RPE and its acceptability indicates a possible beneficial effect of consuming a low GI‐high protein pre‐exercise L meal. (Supported by research grants from the Saskatchewan Pulse Growers and NSERC Canada)
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".