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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".