An Evaluation of Thunderstorm Observations in Southern Ontario Using Automated Lightning Detection Data
Bibliographic record
Abstract
Abstract High-impact weather events, such as thunderstorms and their associated hazards, are aspects of a changing climate that are likely to have an adverse effect on society. Southern Ontario is Canada’s most populated region as well as the region of Canada that receives the most thunderstorms. Before completing climatological studies of thunderstorms in southern Ontario, it is important to determine whether historical thunderstorm data are reliable. Archived thunderstorm data are available from eight 24-h-staffed weather stations across southern Ontario. The data may be subject to observer bias. This study compared the manual observations of thunderstorms with automated data from the Canadian Lightning Detection Network. It was found that the data that are based on the manual observations are reliable enough that any significant trends in thunderstorm occurrence over time should be apparent. Because of the small-scale nature of thunderstorms, however, the data may only be valid for small distances (up to 10 km) around each weather station. A diurnal bias was also discovered, with manual thunderstorm observations being slightly better at night.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".