Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America
Bibliographic record
Abstract
Abstract The Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1) contains 50‐member ensembles of bias‐adjusted near‐surface global and regional climate model variables on a 0.5° grid over North America for historical and future scenarios (1950–2100). Canadian Earth System Model Large Ensembles (CanESM2 LE) and Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) datasets are bias‐corrected using a multivariate quantile‐mapping algorithm for statistical consistency – in terms of marginal distributions and multivariate dependence structure – with two observationally constrained historical meteorological forcing datasets. For each observational dataset, bias‐adjusted variables are provided for two sets of 50‐member initial‐condition CanESM2 ensembles (historical plus RCP8.5 scenarios, 1950–2005 and 2006–2100, respectively; and historicalNAT scenario, 1950–2020, which excludes anthropogenic forcings), and one 50‐member CanRCM4 ensemble (historical plus RCP8.5). The archive includes daily minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation and incoming longwave radiation. Intended uses include hydrological and land surface impact modelling, as well as related event attribution studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".