Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rowing ergometers can be found in most gyms and fitness centers, but many people who use them regularly have little or no instruction in rowing technique. It is not known whether nonrowers who regularly practice ergometer rowing are at risk of musculoskeletal problems. This study was done to quantify the differences in kinematics, kinetics, and musculoskeletal loading of competitive rowers and nonrowers during ergometer rowing. An experiment was performed to collect kinematic, external force, and EMG data during er-gometer rowing by 5 university-level competitive rowers and 5 nonrowers. Kinematic and external force data were input to a 3-D whole-body musculo-skeletal model which was used to calculate net joint forces and moments, muscle forces, and joint contact forces. The results showed that competitive rowers and nonrowers are capable of rowing an ergometer with generally similar patterns of kinematics and kinetics; however, there are some potentially important differences in how they use their legs and trunk. The competitive rowers generated higher model quadriceps (vastus) muscle forces and pushed harder against the foot cradle, extending their knees more and their trunks less than the nonrowers during the drive phase. They also had higher contact forces at the knee and higher peak lumbar and knee flexion moments. The ratio of average peak vastus force to average peak erector spinae force in the experienced rowers was 1.52, whereas it was only 1.18 in the nonexperienced rowers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it