Bibliographic record
Abstract
The Northern Tornadoes Project (NTP) 2024 Annual Report provides a comprehensive overview of the project's operations, research, and findings over the past year. In 2024, NTP continued its mission to improve tornado detection, documentation, and public awareness across Canada. A significant milestone was the establishment of the Canadian Severe Storms Laboratory (CSSL) at Western University, supported by a $20 million contribution from ImpactWX. The CSSL serves as a hub for severe storm research and data collection, integrating multiple projects, including NTP, the Northern Hail Project, and the newly launched Northern Mesonet Project. Key advancements in 2024 included the release of a new tornado dataset (1980–2023) and an Advanced Dashboard for detailed event analysis. NTP recorded 129 tornadoes and 86 downbursts, utilizing ground surveys, high-resolution drone and satellite imaging, and crowdsourced data. Notably, four billion-dollar storms struck Canada, including a record-breaking GTA flash flood and a potential fire tornado in Jasper, AB. Additionally, the Michael Newark Digitized Tornado Archive was launched, preserving historical tornado records. With increased media engagement and scientific publications, NTP remains a leader in severe storm research. Moving forward, the project aims to refine detection methodologies and enhance public safety efforts through data-driven insights.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".