Northern Tornadoes Project. Annual Report 2023
Bibliographic record
Abstract
The Northern Tornadoes Project is now in its 7th year, and its 5th year of covering events right across the country. With all those years under our belts, we are getting a sense of just how variable tornado occurrence is across the country. The 2023 season was a relatively quiet one with 86 documented tornadoes (compared to 129 in 2022 and 124 in 2021). It also featured the lowest number of EF2+ tornadoes - by far - in the NTP era at only 5 (there were 31 and 30 in 2022 and 2021, respectively). Yet even during a quiet year we can have an extreme tornado event – which is what the EF4 tornado near Didsbury, AB on Canada Day was. This tornado flattened the buildings that it encountered (only a small number due to its path through a rural area). Thankfully, occupants of the homes that were destroyed got the warning and found safety. A recent and very successful initiative at NTP has been the hiring of a large number of interns during the summer. They help with surveys or with coding up solutions to various NTP research problems. The quality of the work they have done has been outstanding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.035 |
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".