Canadian Convective Parameters and Lightning Dataset version 1 (CanCPLDv1)
Bibliographic record
Abstract
The Canadian Convective Parameters and Lightning Dataset version 1 (CanCPLDv1) is a comprehensive multi-decade dataset designed to support studies on the future evolution of thunderstorms in North America. Simulating thunderstorms in climate models is challenging due to their small scale, the complexity of physical processes, and the need to parameterize processes within a model subgrid. Changes in thunderstorm activity can be inferred by identifying relevant environmental parameters, such as convective available potential energy, humidity, wind shear, etcetera, and by using statistical techniques to relate these proxies to thunderstorms. Climate model projections of these parameters can then be used with the statistical models to predict future changes in thunderstorm activity. In CanCPLDv1, historical thunderstorm activity is represented by 3-hourly cloud-to-ground and intra-cloud/cloud-to-cloud lightning flash totals from the Canadian Lightning Detection Network (CLDN) (regions north of 40°N on a 0.1° grid), and the thunderstorm environment by 201 convective storm parameters derived from the European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5) (3-hour intervals for all of North America on a 0.25° grid). Gridded CLDN and ERA5 data span the years from 1998-2023. Additionally, the same convective parameters are calculated from Coupled Model Intercomparison Project Phase 6 (CMIP6) HighResMIP climate model simulations at 6-hour intervals for 20-year periods corresponding to 1°C (recent past) and 2°C, 3°C, and 4°C levels of global warming above pre-industrial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".