Bringing Microphysics to the Masses: The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign
Bibliographic record
Abstract
Abstract Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes. The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019/20 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in situ and remote sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K–12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1,000 individuals were reached during the project.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".