The Northern Tornadoes Project – Shifting the Post-Event Paradigm in Canada
Bibliographic record
Abstract
The Northern Tornadoes Project (NTP) was founded in 2017 at Canada's Western University, supported by social impact fund ImpactWX, with the aim of better detecting tornado occurrence, improving severe and extreme weather prediction, mitigating against harm to people and property, and investigating the future implications of climate change. The project was initially limited in scope – attempting to find at least a few undocumented tornadoes in the northern forests of Ontario and Quebec. After demonstrating that the NTP could more than double the annual tornado count in those provinces, the NTP set out to detect, assess and document every tornado that occurs across the country. We also forged other partnerships with Western Libraries, University of Manitoba, York University, Pelmorex’s The Weather Network, Instant Weather, and CatIQ. Research collaborations were undertaken with a number of academic institutions inside and outside of Canada. As the project evolved, the NTP required new techniques and technologies to allow us to meet our ambitious scientific goals. Cutting-edge remote sensing capacity, including ultra high-resolution satellite imagery and piloted and remotely piloted aircraft systems, needed to be utilized and the latest processing techniques adopted. New means of assessing wind damage were employed. Even a new set of definitions related to tornadoes and related phenomenon had to be developed. The societal impacts of significant events are also increasingly being investigated and becoming part of the event record. Interestingly, as our assessment tools reach ever-higher resolution, new problems emerge. A lower-resolution image showing what might have been considered a wide tornado path through the forest in the past now often shows evidence of a mix of tornado and downburst damage, and thorough tree-by-tree analysis is needed to untangle the two. Thus, methods for rating and documenting tornado and downburst damage must also evolve as resolution increases. In addition, the NTP is building new ways of using social media reports of severe weather as crowdsourced data, including creating a community of ‘super-contributors’ that provide high-quality evidence. Sophisticated methods for ‘scraping’ social media for key reports are also under development. As a result of these detection, assessment and documentation processes, we are generating novel research-quality data sets that are being employed by a number of different types of users. All NTP data are open source in order to foster further innovation. The presentation will provide a number of detailed examples showing how the NTP has shifted the post-event paradigm in Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".