Bibliographic record
Abstract
Using the reflectivity and Doppler data of the Carvel radar, located near Edmonton, Alberta, for the summer of 2000 to 2004, we produced a mesocyclone climatology for a region within 120 km from the radar using the mesocyclone detection algorithm of the McGill Radar data Analysis, Processing and Interactive Display (RAPID) software system. The Upper level Vertically Integrated Liquid water content (UVIL) algorithm of the same software package has been used to detect strong convection. Two datasets were built. The first one consists of dates when mesocyclones occurred while the other includes dates characterized by strong convection but without mesocyclonic activity. A synoptic-scale analysis is conducted to identify the main differences between the atmospheric circulations of the two datasets. The upper-tropospheric flow associated with the mesocyclonic events shows a highly amplified meridional circulation. A strong trough-ridge couplet is evident 48 hours prior to the event. This atmospheric feature is responsible for the development of a vorticity maximum that is advected into Alberta. Lee cyclogenesis in noticeable on the sea-level pressure field associated with mesocyclone activity along with a significant low-level warm temperature input in the region of study. The corresponding atmospheric patterns associated with non-mesocyclonic events do not present such large-scale precursors. The different atmospheric fields act to favour large-scale forcing for ascent when mesocyclones are going to occur.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".