Measuring winter precipitation and snow on the ground in northern polar regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measuring winter precipitation in cold and windy regions is recognized as a difficult task. Nonetheless, the accurate measurement of solid precipitation provides important input data for predicting snowmelt floods and avalanche danger and for monitoring climate change. The difficulties in measuring solid precipitation are associated with environmental factors and technological issues. Environmental factors that contribute to measurement errors include wind, freezing rain, rime, and a large range of solid particle shapes and sizes. Technological issues include gauge configuration, the need for remote, low-power-consumption operation, and difficult conditions for data transmission and retrieval. The objectives of this study were to review currently used gauges for measuring solid precipitation and snow on the ground, to summarize the positive and negative characteristics of each gauge, and to provide a discussion of best practices and design and performance criteria that might be used to stimulate research on new and/or improved precipitation gauges in Northern Research Basin (NRB) countries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it