Ouranos Ensemble of Bias-adjusted Simulations - Global models CMIP6 - AHCCD v3 (ESPO-G6-AHCCD v1.0.0)
Bibliographic record
Abstract
This dataset is an ensemble of CMIP6 climate model bias-adjusted simulations for total daily precipitation, minimum, maximum and mean daily temperature over the period 1950–2100. Daily time series are extracted at 692 locations for temperature, and 439 for precipitation, corresponding to weather stations from the "Adjusted and homogenized Canadian climate data" (AHCCD) published by Environment and Climate Change Canada (ECCC). Climate simulations grids are bilinearly interpolated at each station with a 1.5° buffer zone, then bias-adjusted using the detrended quantile mapping (DQM) method and AHCCD as the reference dataset. This ensemble has two distinguishing features. It is bias-adjusted at the weather station scale, namely all AHCCD stations with at least 25 years within a period of 35 years with valid months according to WMO criteria (a month is considered valid if it has has less than 5 consecutive missing days, and less than 11 missing days overall). Second, it includes a large number of model realizations from ScenarioMIP simulations (627 for precipitation and 561 for temperature) across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). CMIP6 simulations were acquired via the zarr Pangeo Catalog (https://pangeo-data.github.io/pangeo-cmip6-cloud/overview.html) and biasdajusted with the xscen package (https://xscen.readthedocs.io/en/latest/). This dataset has been created as part of project funded by Infrastructure Canada assessing the likelihood of future climate hazards to built infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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; both teacher heads agree on what is shown here.
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".