Bibliographic record
Abstract
In the last century, participation in physical activities has developed dramatically. The best documented development was in running with millions of participants (6, 24). Between 1978 and 1983, the number of runners in Canada has more than doubled from 15 % to 31 %, but has decreased in 1988 to about 18 % of the total population (50, 56). The high incidence of injuries in runners has been proposed as one possible reason for this decrease. Between 37 to 56 % of all runners are injured during a year of running (31) and running injuries make up the majority of sport related injuries in the young (31.5 %) and the old (40.5 %) physically active population (29). Major reasons for the development of exercise related injuries proposed in the literature include previous injuries, training errors, excessive impact forces and excessive foot movement or movement control (8, 9, 21, 31). From a biomechanical point of view impact forces and movement control are of interest since they can be influenced with the sport shoe. This paper will concentrate on these two aspects and propose two new paradigms for the functional understanding of impact forces and movement control.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".