Bibliographic record
Abstract
This dataset combines crowd-sourced hail reports in Canada between 2005 and 2024 from two internal sources collated by Environment and Climate Change Canada. Time is in UTC. See Provinces and territories of Canada - Wikipedia for province codes. Common reference objects are used to compare with the diameter of the largest hail stone in vicinity. Modifications from 2005-2022 v1.0.0:- Extension of the database from 2005-2022 to 2005-2024. - Inclusion of METAR/SPECI hail/graupel reports (TSGR and TSGS) for 2023-2024 (source: Iowa Environmental Mesonet). Notes:1. There are no (exact) duplicated entries, and the time, location and hail size were checked, but they were not validated against external data sources. 2. One novelty is the systematic addition of METAR/SPECI reports with the extraction of the hail size from the RMK (71 reports, most provide unrealistically small hail diameter and the diameter is not always consistent with the GS/GR code, the default is GS “small hail/graupel” = 3mm and GR “hail” = 10mm if the diameter is not included in RMK). 3. There will be more frequent data from Quebec province than in 2005-2022 because of a better filtering of the French word for hail (grêle). So please do not use the data for studying climatic trends (plus all the usual warnings about using crowd-sourced data).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.039 |
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".