Bibliographic record
Abstract
The aim of this study was to determine whether cyclists modify the pattern of force application to become more effective during a prolonged ride to exhaustion. Twelve competitive male cyclists completed a steady-rate exercise ride to exhaustion at 80% of their maximum power output at 90 rev x min(-1) on a cycle ergometer. Pedal force, pedal and crank angle data were collected from an instrumented bicycle for three pedalling cycles at the end of the first and final minutes of the exercise test with simultaneous video recording of the lower limbs. Kinematic and force data were combined to compute hip, knee and ankle joint moments. There were changes in the pattern of force application, joint kinematics and joint moments of force. Comparison of the first minute and the final minute ride revealed significantly increased peak effective force (340 +/- 65.0 and 377 +/- 74.8 N for the first and final minute, respectively; F1,11 = 7.44, P = 0.02), increased positive (28.4 +/- 4.5 and 30.5 +/- 4.8 N x s for the first and final minute, respectively; F1,11 = 7.80, P = 0.02) and negative angular impulses (-1.5 +/- 1.6 and -2.4 +/- 1.5 N x s for the first and final minute, respectively; F1,11 = 4.50, P = 0.06). Contrary to our initial assumptions, it would appear that riders became less effective during the recovery phase, which increased the demand for forces during the propulsive phase. Training the pattern of force application to improve effectiveness may be a useful strategy to prolong an endurance ride.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".