Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 2
Bibliographic record
Abstract
This data set provides monthly summary climate data at 1-km x 1-km spatial resolution for four Daymet variables; minimum and maximum temperature, precipitation, and vapor pressure. These single month summary data products are produced for each month for individual years and covers the period of record from 1980 to 2014. The monthly climatological summaries are derived from the much larger data set of daily weather parameters (Thornton et al., 2014), produced on a 1-km x 1-km grid over the conterminous United States, Southern Canada, and Mexico as station data inputs allow (Thornton, et al., 2014). Daymet monthly summary data are available from the ORNL DAAC via two download mechanisms: 1. Search and Order or FTP Browse: Files are in both netCDF version 4.0 format or GeoTIFF file formats. There are a total of 1,680 *.nc4 files and 1,680 .tif files for the four Daymet parameters (prcp, tmax, tmin, and vp) for 35 years (1980 -2014). 2.THREDDS (Thematic Real-time Environmental Data Services) Data Server: Data can be subset spatially and temporally prior to downloading. Subsetting and downloading of files available through THREDDS has a 2-GB file size limitation.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".