Reverse Lactate Threshold: A Novel Single-Session Approach to Reliable High-Resolution Estimation of the Anaerobic Threshold
Bibliographic record
Abstract
The multisession maximal lactate steady-state (MLSS) test is the gold standard for anaerobic threshold (AnT) estimation. However, it is highly impractical, requires high fitness level, and suffers additional shortcomings. Existing single-session AnT-estimating tests are of compromised validity, reliability, and resolution. The presented reverse lactate threshold test (RLT) is a single-session, AnT-estimating test, aimed at avoiding the pitfalls of existing tests. It is based on the novel concept of identifying blood lactate's maximal appearance-disappearance equilibrium by approaching the AnT from higher, rather than from lower exercise intensities. Rowing, cycling, and running case data (4 recreational and competitive athletes, male and female, aged 17-39 y) are presented. Subjects performed the RLT test and, on a separate session, a single 30-min MLSS-type verification test at the RLT-determined intensity. The RLT and its MLSS verification exhibited exceptional agreement at 0.5% discrepancy or better. The RLT's training sensitivity was demonstrated by a case of 2.5-mo training regimen following which the RLT's 15-W improvement was fully MLSS-verified. The RLT's test-retest reliability was examined in 10 trained and untrained subjects. Test 2 differed from test 1 by only 0.3% with an intraclass correlation of 0.997. The data suggest RLT to accurately and reliably estimate AnT (as represented by MLSS verification) with high resolution and in distinctly different sports and to be sensitive to training adaptations. Compared with MLSS, the single-session RLT is highly practical and its lower fitness requirements make it applicable to athletes and untrained individuals alike. Further research is needed to establish RLT's validity and accuracy in larger samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".