Bibliographic record
Abstract
The precipitations on meteorological scanning radar may comes from different altitudes and different process. The challenge for operational meteorology is to assess the part of this precipitation which will reach the ground and at what place. Many factors influence the difference between radar data and ground data: partial beam filling, attenuation and beam blocking, bright band enhancement, wind transport of the precipitation, growth or decay of the drops/flakes below the lowest elevation angle of the radar. An important case for operational meteorology is that of light snow aloft whose base has an horizontal slope toward the ground: "snow virgas". I will use the output of two vertical pointing radar in this thesis to find what happens in those trails and try to explain the influencing mechanisms. I will also describe an algorithm that attempts to predict the place where the snow will reach the ground using McGill University scanning radar. My study shows that the slope of the snow virgas is essentially due to the transport by winds in saturated airmasses and evaporation of flakes in unsaturated ones. Finally, finding the slope of the virgas toward the ground, by an automatic algorithm, is extremely difficult on a scanning meteorological radar due to its coarse resolution.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".