<i>ABSTRACTS</i> 7 <sup>th</sup> World Congress of Mountain & Wilderness Medicine A combined meeting of the International Society for Mountain Medicine and the Wilderness Medical Society July 30–August 4, 2016 Telluride, Colorado
Bibliographic record
Abstract
Every two years, the International Society for Mountain Medicine (ISMM) holds its World Congress of Mountain Medicine.The meetings attract scientists and clinicians interested in high altitude medicine, biology, and rescue operations, who provide care and promote safety and health in mountainous regions around the world.Every four years, the Wilderness Medical Society (WMS) sponsors a World Congress of Wilderness Medicine that deals with the latest research and clinical aspects of all wilderness environments.In 2016, the two organizations joined forces for the 7th World Congress on Mountain and Wilderness Medicine.Presentations by global experts addressed altitude medicine, mountain rescue, diving medicine, wilderness education, emerging diagnostic methods, improvised rescue techniques, disaster medicine, neglected tropical diseases, and more.The abstracts selected for publication are divided between High Altitude Medicine & Biology, featuring ISMM members and topics, and Wilderness & Environmental Medicine, featuring WMS members and topics.There is some subject overlap, and the reader is encouraged to refer to both journals for a complete picture of the breadth and excellence of work in these fields.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.249 |
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".