Bibliographic record
Abstract
Rising temperatures bring rising numbers of outdoor training athletes. No athlete, however, is as visually obvious as a runner. The Red River Valley has seen a growing presence of runners over the past few years. With Runner's World pegging the Fargo Marathon as one of the best marathons in the United States, the Red River Valley has become a hot spot for cross-country, track, amateur marathoners, and road racers. One such race in the Red River Valley is the annual 4th of July Firecracker in Grand Forks. The event includes a walk/run 5K and 10K (2011 race results). The Firecracker race formerly included a half marathon. However, the half marathon component has been moved to a separate event during the Cats Incredible festivities each August. The events are indicative of a growing market for such races. Danielle Thompson, a Grand Forks native and first place women’s finisher at this year's Firecracker race discussed the race and her thoughts on the Grand Forks running community. “I’ve noticed a definite increase in the amount of runners in the Red River Valley area. It seems like more people have caught the running bug and are joining road races. My mom actually signed me up for it and so I had planned on just enjoying a nice race with her. I felt really good throughout [the race].” The events provide an outlet for former high school and collegiate cross-country runners and running enthusiasts to competitively engage within their community. Showing the growing running trend, winter events have even grown out of rising interest - an event titled “Frozen Feat,” a 5k/10k in February where the average temperature is about 10 degrees F. Another runner, Melissa Ramsey, also ran the 4th of July race. “Going into the Firecracker 5K, my goal was to jog/run as much of it as I could. It was my first race event in a long time.” Ramsey continued, “I really surprised myself in how motivated I was to keep going. I usually walk/jog/run with [my dog] Bosko. He is very energetic and always keeps me going.” The Red River Runners organization has long been an organizer for amateur runs in the Red River Valley and boasts an enrollment of 758 on its mailing list. In addition to the planning acumen required to coordinate large group runs twice weekly, the organization also is involved in helping individuals prepare for the Fargo Marathon from January until the end of May, when the event is hosted. Also at the Firecracker 5k/10k, Professor of Social Work Bret Weber discussed his growth as a runner both inside and outside Grand Forks. He stated, “I found that I loved running one year before I moved here [from Utah]… when I moved here, I needed to replace the mountains and I found that I loved running in the wind.” Regardless of the role of summer in bringing out runners, it is clear that running in Grand Forks and the surrounding area will not decline in popularity within foreseeable future.
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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 | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.255 | 0.109 |
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".