Bibliographic record
Abstract
Outside the realm of scientists, environmentalists, and activists, a surprising but dynamic group of individuals is equally invested in the health of the Salish Sea. This group of “aquatic animals” puts our bodies where our beliefs are by swimming in Puget Sound. Our group includes elite swimmers who have crossed the Straight of San Juan de Fuca as well as "weekend warriors" who swim along Alki Beach year-round, many without wetsuits. Because of our passion for our sport, we partner with community groups, learn from cutting-edge science, and advocate for the health of Puget Sound. We comprise the “total immersion” piece of the advocacy puzzle. This unique, 12-minute overview highlights how a successful small business has revived the sport of open-water swimming in Puget Sound and how this supports essential environmental action. As a result of the session, the audience will: • Discover the explosion of swim races and other events in the Salish Sea, from Canada and the US; • Creatively imagine how to leverage the recreational use of Puget Sound into educational messages; • Brainstorm ways to partner with and exploit the burgeoning business of open-water swimming in the Salish Sea.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".