The CSSL – A New Era in Severe Storms Data and Research in Canada
Bibliographic record
Abstract
The Canadian Severe Storms Laboratory (CSSL) was launched at Western University in October of 2024 with the aim of being the authority for severe convective storm (SCS) data and research in Canada. The CSSL will advance the detection, documentation and understanding of SCS and their impacts across the country.The CSSL’s mission is currently driven by three key projects: the Northern Tornadoes Project, the Northern Hail Project, and the Northern Mesonet Project. The Northern Tornadoes Project aims to improve tornado, downburst and derecho detection and documentation across Canada, utilizing aerial and ground surveys, satellite imagery and advanced research methods to improve the Canadian climatology and analyze trends.The Northern Hail Project focuses on understanding hailstorm frequency, intensity, and impacts, leveraging radar observations, hail collection and damage assessments to better characterize hail hazards.The Northern Mesonet Project supports these initiatives by increasing the spatial density of real-time advanced weather observations, enhancing data availability, and improving data quality for SCS analysis and prediction.It is anticipated that an additional project, focused on flash flooding hazards related to SCS, will be launched under the CSSL banner in the coming years. Some work has already begun in that area.The CSSL also provides unique training opportunities through its graduate student and internship programs. These programs aim to cultivate the next generation of SCS researchers by offering hands-on experience with in-field data collection, techniques development and applied research.This presentation will outline the strategic framework and technological advancements that underpin the CSSL’s operations and research. It will also showcase the latest findings from each project and explore future directions.
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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".