Blown Away: Interns Experience Science, Research, and Life on Top of Mount Washington
Bibliographic record
Abstract
Abstract With extreme winds, rapidly changing weather, and myriad weather conditions during any given month, Mount Washington, New Hampshire (1,917 m MSL), is an ideal location to observe and learn about atmospheric sciences. During the summer of 2013, Mount Washington Observatory (MWO) welcomed a select group of interns to experience life at the “Home of the World’s Worst Weather” and develop scientific and meteorological skills. The goals of the internship program are to learn how to observe and forecast mountain weather; develop data analysis and critical thinking skills through individual research projects; and live, work, and collaborate effectively with others at a remote mountain-top observatory. Interns are typically undergraduate students or recent graduates of atmospheric science programs and are selected from a highly competitive field of applicants. The summer 2013 interns worked on a variety of research projects, ranging from developing a forecast tool for the gustiness of wind at the summit to understanding the evolution of atmospheric and environmental conditions that lead to avalanches in nearby Tuckerman Ravine. To accomplish their research projects, the interns learned how hourly weather observations are made, used data analysis software, and practiced critical thinking about their methods and results. Weekly meetings with the interns and the MWO director of research allowed for the sharing of research progress, peer feedback, and practice presenting scientific results. The internships ended with presentations of their scientific research to MWO observers, staff, and observatory members. Post-internship survey responses revealed the program was highly effective at meeting its goals and provided constructive suggestions for future internship programs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".