Validity and Reliability of the Computrainer Lab™ During Simulated 40 and 100 km Time-Trials
Bibliographic record
Abstract
(CT) was assessed, for the first time, using a high-precision motor-driven calibration rig during simulated variable intensity 40 and 100 km time-trials (TTs). The load patterns imposed by the CT were designed from previously published studies in trained cyclists and included multiple 1 or 4 km bursts in power output. For the 40 and 100 km TTs, cluster-based analyses revealed a mean measurement error from the true workload of respectively 0.7 and 0.9%. However, measurement errors were dependent upon the workload variations, fluctuating from 0.2 to 5.1%. Average biases between repeated trials were contained within ± 1.1% for both TTs. In conclusion, using 40 and 100 km TTs containing 1 or 4 km bursts in power output, the present results indicate that (1) the CT can reliably be used by scientists to determine differences between research interventions; (2) the CT provides valid results of power output when data are being analyzed as a whole to derive one mean value of power output and; (3) variations in workload make it difficult to determine at any one time the veracity of the true power output produced by the athlete.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".