Canadian climate data portals: A comparative analysis from a user perspective
Bibliographic record
Abstract
Climate data portals are essential tools for climate change adaptation. This study analyses differences between two Canadian portals providing bias-adjusted CMIP6 simulations: Climate Data Canada and Portraits Climatiques. The study evaluates three core variables (daily maximum temperature, daily minimum temperature and precipitation) as well as assesses five case studies, taken from the agriculture, transport and health sectors, that relied on climate indicators available through the portals. The underlying datasets vary in multiple ways (bias-adjustment methodology, climate of reference, ensemble composition, emissions scenarios) and, in general, the climatology of variables and indicators tends to be statistically different between portals towards the end of the century. Differences are significantly reduced when comparing projected changes with respect to present climate conditions, highlighting the important role played by the dataset used as a reference for the bias-adjustment procedure. When considered from the point of view of practical applications, the discrepancies between the portals are generally, although not always, sufficiently small that they do not impact the resulting decisions. Finally, indicators based on a fixed threshold were found to be strongly influenced by the reference used for the bias adjustment.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".