Are users drawing the same conclusions from different climate data portals?
Bibliographic record
Abstract
Climate data portals serve as important tools for climate services providers to effectively communicate with decision-makers. With a rapid increase in the number of such portals and the datasets underlying them, critical questions arise: do users encounter similar narratives across these platforms, and does their choice of portal influence decisions on adaptation measures? This presentation conducts a comparative analysis of two prominent Canadian portals, namely Climate Data Canada and Portraits Climatiques. Both portals feature bias-adjusted CMIP6 simulations that differ in various aspects, including bias-adjustment methodology, climate of reference, ensemble composition, and emissions scenarios. The impact of these choices is explored by assessing three core variables (daily maximum temperature, daily minimum temperature, and daily precipitation) and examining five case studies from the agriculture, transport, and health sectors. Our findings reveal significant disparities between the portals in terms of climate indicator values at the end of the century, while projected changes compared to the present climate are often more similar. Moreover, we observe a strong influence of the reference dataset choice on threshold-based indicators. Despite these discrepancies, users commonly make similar final decisions when employing both platforms, as adaptation measures are not markedly sensitive to the distinctions and as other non-climate-related factors must be considered in the decision-making process. This study sheds light on differences and similarities between climate data portals, emphasizing the need for climate services organizations to transparently communicate the implications of their choices to users, in order to guide the formulation of effective adaptation strategies.
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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 | 0.061 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".