North American Regional Climate Change Assessment Program dataset
Bibliographic record
Abstract
The North American Regional Climate Change Assessment Program (NARCCAP) is a collection of regional climate model simulations downscaling global simulations from CMIP3 to 50-km resolution over North America. The collection was generated in 2007-2012 with the goal of investigating uncertainties in regional scale projections of future climate and generating climate change scenarios for use in impacts research. NARCCAP comprises a set 12 simulations from 6 RCMs downscaling 4 GCMs using a fractional factorial design, plus 1 simulation from each RCM downscaling the NCEP reanalysis, and 2 global atmosphere-only timeslice experiments. Historical data spans 1971-2000, and future data 2041-2070 using the SRES A2 emissions scenario. It includes more than 3 dozen 2D variables and a half-dozen 3D variables at 3-hourly frequencies, plus a handful of static and daily variables. All data is at 50-km spatial resolution over a domain that covers most of North America and is stored in CF-compliant netCDF files. More detailed documentation of the dataset is available at: https://narccap.ucar.edu
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".