Bibliographic record
Abstract
This is the sixth proceedings based on a series of presentations addressing issues of safety, injury prevention, and decreasing the risk of catastrophic injury in ice hockey—the first being published from the symposium held in 1987 at Montreal, Canada. These symposia have been held every five-to-six years, which we feel is an adequate time for new ideas to be formulated and new research to come to the forefront. The idea of the symposium is to present the latest findings, research, and countermeasures related to safety in ice hockey. The symposium provides opportunity for discussions by all those that participate in the game of hockey and promotes the development of both short-term and long-term strategies for decreasing the risk of both acute and long-term injury, particularly brain injury. The topic of brain injury is not confined to ice hockey, and other sports are facing similar issues related to how the game is played. For this reason, we have much to learn from each other and these presentations bring together researchers from a diverse group of fields, including sports medicine, bioengineering, mechanical engineering, neuropsychology, sports litigation, and sports epidemiology.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.920 | 0.882 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".