Bibliographic record
Abstract
Three years ago, we set our sights on finding at least a few undocumented tornado tracks in the remote forests of northern Ontario. We have covered greater distances and nurtured bigger ambitions since then. From northern Ontario to all of Canada, from aircraft surveys to drones and satellites, from on-the- ground damage investigations to artificial intelligence analyses, the Northern Tornadoes Project is one of the most comprehensive tornado research projects in the country. It aims to better detect tornado occurrences throughout Canada, improve communication of tornado science and risk, and mitigate against harm to people and property. NTP also seeks to increase knowledge of tornado climatology to better understand trends due to climate change. This report is our journey through the past three years. It tells you where we have been, and where we are headed. The Northern Tornadoes Project took off through generous donations from Toronto-based social impact fund ImpactWX and Western University. The funds got the project started, and helped expand it from one province to the whole nation. We also acquired cutting-edge technology, and built an expert team of researchers, engineers, and meteorologists. This includes collaborations with Environment and Climate Change Canada, and research groups in Canada, United States, and the United Kingdom.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.032 |
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".