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
Abstract Ice-phase precipitation occurs at Earth’s surface and may include various types of pristine crystals, rimed crystals, freezing droplets, secondary crystals, aggregates, graupel, hail, or combinations of any of these. Formation of ice-phase precipitation is directly related to environmental and cloud meteorological parameters that include available moisture, temperature, and three-dimensional wind speed and turbulence, as well as processes related to nucleation, cooling rate, and microphysics. Cloud microphysical parameters in the numerical models are resolved based on various processes such as nucleation, mixing, collision and coalescence, accretion, riming, secondary ice particle generation, turbulence, and cooling processes. These processes are usually parameterized based on assumed particle size distributions and ice crystal microphysical parameters such as mass, size, and number and mass density. Microphysical algorithms in the numerical models are developed based on their need for applications. Observations of ice-phase precipitation are performed using in situ and remote sensing platforms, including radars and satellite-based systems. Because of the low density of snow particles with small ice water content, their measurements and predictions at the surface can include large uncertainties. Wind and turbulence affecting collection efficiency of the sensors, calibration issues, and sensitivity of ground-based in situ observations of snow are important challenges to assessing the snow precipitation. This chapter’s goals are to provide an overview for accurately measuring and predicting ice-phase precipitation. The processes within and below cloud that affect falling snow, as well as the known sources of error that affect understanding and prediction of these processes, are discussed.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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