Estimation of the Maximal Lactate Steady State in Junior Soccer Players
Bibliographic record
Abstract
This study aimed to predict the velocity corresponding to the maximal lactate steady state (MLSS(V)) from non-invasive variables obtained during an incremental maximal running test (University of Montreal Track Test, UMTT) and to determine whether a single constant velocity test (CVT), performed several days after the UMTT, could estimate the MLSS(V). During a period of 3 weeks, 20 male junior soccer players performed: (1) a UMTT, and (2) several 20-min CVTs to determine MLSS(V) to a precision of 0.35 km·h(-1). Maximal aerobic velocity (MAV) and velocity at 80% of maximum heart rate (V80%HRmax) were strong predictors of MLSS(V). A regression equation was obtained: MLSS(V)=(1.106·MAV) - (0.309·V(80%HRmax)) - 3.024; R2=0.60. Running velocity during CVT (V(CVT)) and blood lactate at 10 (La10) and 20 (La20) minutes further improved the MLSS(V) prediction: MLSS(V)=V(CVT)+0.26 - (0.812·ΔLa(20-10)); R2=0.66. MLSS(V) can be estimated from MAV and V(80%HRmax) during a single incremental maximal running test among a homogeneous group of soccer players. This estimation can be improved by performing an additional CVT. In terms of accuracy, simplicity and cost-effectiveness, the reported regression equations can be used for the assessment and training prescription of endurance in team sport players.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".