Bibliographic record
Abstract
strikes at a critical time. And infections are most common immediately before or after high-level competition. Diffl am throat spray was tested as a way to pre-vent upper respiratory symptoms in asso-ciation with a half-marathon race3 (see page 127). Economics in sports and exercise medicine – here to stay! A Canadian systematic review (on which I am an author) has already been tabled in the New Zealand parliament as sensi-ble folks tried to help Prime Minister John Key make a quality decision. The issue was to keep funding exercise classes, which save the nation money by reduc-ing fall-related injuries in seniors4 (see page 80). The days of anyone arguing that that health economics is not sports medicine are patently over. Ask the Australasian College of Sports Physicians as they negotiate with the Rudd gov-ernment to fund their specialty – a spe-cialty that has the potential to limit the economic burden of physical inactivity. Physical inactivity costs the US over $1 trillion annually; clearly exercise is medi-cine – and good value at that. Conference preview – book now for AMSSM in Cancun! Sign up to be a part of AMSSM’s excel-lent conference in Cancun, in Mexico’s Mayan Riviera. Your family will love you for that! At this friendly and value-packed conference you will hear the latest from
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.454 | 0.364 |
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".