Northern Tornadoes Project. Northern Tornadoes Flyover Project: Summary Technical Report of the Year 1 Pilot Study
Bibliographic record
Abstract
12 January 2018 The objectives of the Year 1 Pilot Study were to (i) develop a methodology for determining tornado occurrence in Northern Ontario, and (ii) obtain research quality data for at least one event. Because of the isolation of many regions, the approach assumed the use of radar data analysis combined with aerial surveys. These objectives were achieved. Aerial surveys were conducted for a total of seven events in Ontario and southern Quebec and 15 confirmed or probable tornadoes identified. Archival geo-tagged imagery was obtained for six of these events. Ten confirmed or probable tornadoes were identified in Ontario, five of which were not in the OSPC database. In addition, 5 tornadoes were confirmed in Quebec. For the 2017 season, the OSPC had a list of 10 verified tornadoes, as of December 21, 2017. The pilot project raises this number to 15. In total, 4 EF2 tornadoes and 1 EF3 tornado were identified via aerial photography. The remainder were EF1 or EF0. UPDATE – 23 April 2018 Based on the analysis of newly available Planet.com high-resolution satellite imagery and related tools, several events were reassessed and a number of additional tornadoes were discovered. Overall, an additional three tornadoes were added to 2017 count. The updated events are listed in a revised 2017 summary table appended at the end of this document.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".