Bibliographic record
Abstract
This dataset is in HDF5 format. The data was pulled from the ECMWF reanalysis datasets "ERA5 hourly data on single levels from 1940 to present" and "ERA5 hourly data on pressure levels from 1940 to present." Each file in "era5_by_canadian_hail_event" is associated to a specific Canadian hail event and combines the relevant data from both ERA5 datasets. The hail events were created based on the 7000 Canadian hail reports contained in "Integrated Canadian Hail Database (2005-2022)", where we considered reports to be of the same event if they were within a specified window in time and space (7 hours by 256 km). The 7000 reports were grouped into 2092 hail events. Thus, our ERA5 based dataset contains 2092 files. For more information on how the data was handled, see the GitHub repository "era5-based-hail." Each file in this dataset contains the following variables from "ERA5 hourly data on single levels from 1940 to present" : cp [m]: Convective precipitation d2m [K]: Dewpoint temperature at 2m height sp [Pa]: Surface pressure t2m [K]: Temperature at 2m height tcc [100%]: Total cloud cover tciw [kg m-2]: Total column vertically-integrated cloud ice water tclw [kg m-2]: Total column vertically-integrated cloud liquid water tcrw [kg m-2]: Total column rain water tcsw [kg m-2]: Total column snow water tcwv [kg m-2]: Total column vertically-integrated water vapour tcw [kg m-2]: Total column water tp [m]: Total precipitation u10 [m/s]: u-component of wind at 10m height v10 [m/s]: v-component of wind at 10m height And following variables from "ERA5 hourly data on pressure levels from 1940 to present" : r [%]: Relative humidity t [K]: Temperature u [m/s]: Longitudinal wind v [m/s]: Latitudinal wind z [m^2/s^2]: Geopotential at pressure levels [hPa]: 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".