Northern Tornadoes Project. Annual Report 2022
Bibliographic record
Abstract
NTP’s fourth year of detecting, surveying and documenting tornadoes and other damaging wind events across Canada saw a return to a more familiar pattern. In 2021, only two tornadoes were recorded across the Prairies over the 60 days with the highest climatological frequency (mid-June to mid-August).In 2022, 39 tornadoes were confirmed there, with 33 of them during that peak period. However, a catastrophic spring derecho set the stage for a very different and very active season in Ontario and Québec. The May 21st derecho is now one of the most deadly and costly thunderstorm events on record in Canada. While over a billion dollars in insured losses was recorded, 12 people lost their lives and at least another 12 were injured. The storm’s damage path extended over 1,000 km across the most densely populated region in Canada. Though NTP field teams were deployed shortly after the event occurred, it took the rest of the summer to fully investigate this devastating event. Through the rest of the season, Ontario and Québec recorded 77 tornadoes, nearly double what was recorded across the entire Prairies. And the total number of tornadoes across Canada during the 2022 season is tied for the highest we’ve recorded at 117 – and closer to the 150 or so that we thought might be occurring based on statistical analysis. So, it appears that the number of 'missing tornadoes' is indeed dropping due to the efforts of NTP.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.037 |
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".