Xclim: Climate Data Processing and Analysis for Everyone
Bibliographic record
Abstract
Xclim is a climate analysis library developed by Ouranos since 2018, with funding from Environment and Climate Change Canada. It forms the core of the ClimateData.ca climate data analysis platform, offering customizable climate indicators for Canadian climate projections. Xclim was created to address key challenges in climate change data processing, including handling terabyte- and petabyte-scale model outputs, tailoring indicators for specific industries affected by climate change, and providing statistically relevant information for non-specialists seeking to understand climate impacts. Leveraging the power of xarray and dask, xclim equips researchers with unique tools, including climate model ensemble selection, bias adjustment, climate data quality checks, adherence to Climate and Forecast Convention standards, and the ability to compute over 150 pertinent climate indicators across vast datasets. Whether you're an experienced climate scientist or new to the field, this presentation offers valuable insights into harnessing xclim to unlock the potential of climate data, enabling data-driven decisions and contributing to a sustainable future.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.163 |
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".