Bibliographic record
Abstract
The United States Air Force Academy at Colorado Springs, Colorado, welcomed over 300 athletes to the 2018 Warrior Games. Each branch of the United States military was represented along with warriors from the United Kingdom, the Australian Defense Force, and the Canadian Armed Forces. This Paralympic style competition featured competition in archery, cycling, track and field, shooting, powerlifting, sitting volleyball, and wheelchair basketball. Over 70 technology professionals helped bring the Warrior Games to those attending with live closed-circuit broadcasts to each of the major competition venues across the USAFA campus. More than 34,000 people attended the Opening Ceremonies in Falcon Stadium, capped off with performances by Kelly Clarkson and comedian Jon Stewart. Family was central to these Games, as attendance was free, and a special Expo area was dedicated for interaction with sport equipment, vendors for sport organizations, and other adapted sport activities (i.e., wheelchair rugby, wheelchair tennis for adults, youth, and children). The U.S. Air Force won the most gold medals with 70, followed by the U.S. Marine Corp with 44 and U.S. Navy with 41. There were over 475 total medals awarded across all U.S. military branches. The 2018 Warrior Games were competitor tough and family oriented; a quote from former NFL great Herschel Walker summed it up the best, “Family is key to these athletes’ success...we have to do more for these veterans…family must always be included.” Subscribe to Palaestra
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.291 | 0.149 |
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".