Bibliographic record
Abstract
Dear Editor-in-Chief: I read with great interest the recent study by Bendiksen et al. (2) that provided the physiological responses to the Copenhagen Soccer Test (CST) in male soccer players. I applaud the researchers for creating such a highly specific soccer test that encompasses the technical elements as well as the physical demands of high-level soccer. Yet the authors failed to acknowledge that the design of this test is only specific to high-level male soccer players and not applicable to their female counterparts. The physical components of the CST (i.e., distances covered) were selected based on motion analysis studies of male soccer players (1,6), which use speed thresholds to delineate the intensity of locomotor activities performed in a match (e.g., >25 km·h−1 is considered sprinting). The motion analysis characteristics of women’s soccer (4,5) differ compared with men’s; moreover, the application of sprint and high-speed thresholds developed from men’s soccer (1) does not consider the notable sex differences in sprint speed (7) and puts into question the accuracy of the high-speed outcomes reported for female soccer players. Indeed, I have recently questioned this approach to the female soccer motion analysis because there are implications for the sprint and high-speed distances performed (8). I am hopeful that investigators will recognize these sex differences and begin to use knowledge about the speed characteristics (9) and game speed (3) of female soccer players to ensure that there is accuracy in motion analysis reports. In turn, a female-friendly version of the CST could be developed and validated. In the future, care should be taken to characterize the population for which study outcomes are intended; otherwise, practitioners may erroneously implement the CST in female soccer players only to acquire outcomes with little or no relevance. Jason D. Vescovi, PhD York University Toronto, ON, Canada
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 | 0.005 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".