Simultaneous assessment of debris trajectories to determine characteristics of the July 1, 2023 Didsbury, Alberta, Canada EF4 tornado
Bibliographic record
Abstract
The Enhanced Fujita scale is a useful assessment tool to determine wind speeds in tornadoes in Canada, the United States of America, and Japan. However, it is a damage-based assessment which relies on the tornado hitting damage indicators whose wind loads have been well-documented and researched (such as houses, mobile homes, and trees). Additionally, the Enhanced Fujita scale is unable to determine any additional characteristics of the tornado such as the ratio of wind speeds (swirl ratio), or the dimension of the core radius.In a previous study, the authors of this abstract developed a forensic tool to analyse the trajectories of large compact objects (vehicles, farming equipment, trailers, haybales, etc) identified during damage surveys. By combining analytical tornado wind field models with debris equations, the lofting wind speeds required to recreate these observed trajectories can be estimated. This was achieved by utilizing a Monte Carlo simulation to randomly select parameters and plotting cumulative distribution functions to depict the likelihood of lofting at various wind speeds. Analyzing debris trajectories from several documented tornadoes in Canada revealed that this method yields threshold lofting wind speeds comparable to those estimated by other approaches. However, incorporating trajectories resulted in higher estimated lofting wind speeds than the EF-scale ratings derived from ground survey assessments based on structural damage.All of the debris trajectories examined in the previous study were analysed as single independent instances. For example, in the EF4 Didsbury, AB tornado, although the focus was on a single combine harvester, there were multiple other large compact objects in the same area that were not analysed in the study. Additionally, the effect of the relative location of the object to the centre of the tornado was not considered. The goal of this paper is to continue the development of a forensic tool for debris trajectories of large compact objects by analyzing the trajectories of multiple debris objects simultaneously. Analyzing multiple debris simultaneously narrows the range of possible tornadic parameters, which provides more insight to the characteristics of a tornado. Data from the EF4 Didsbury, AB tornado are applied as a case study to test this forensic tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".