Daymet: Gridded subdaily weather data for North America
Bibliographic record
Abstract
A core requirement for many ecosystem modeling approaches is surface weather fields, including temperature, precipitation, humidity, and incident solar radiation. Carbon dynamics and flux estimates from process models depend strongly on daily and subdaily weather conditions. One common obstacle to model implementation over continental scale regions is the difficulty of obtaining the relevant surface weather observations from in situ networks, and producing spatially interpolated (gridded) surfaces of the necessary weather fields at the appropriate spatial and temporal resolution. One approach that has been developed to overcome this obstacle is Daymet, a numerical method for producing gridded surfaces of subdaily temperature (daily maximum and minimum temperature), and daily precipitation, humidity, and radiation over large regions of complex terrain, using daily surface weather observations and an accurate elevation grid as input. We are providing a high-quality gridded surface weather product over North America for input to NACP process modeling studies by expanding on the conterminous U.S. Daymet domain to include Canada (south of 52N) and Mexico. Download Daymet Data: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1219. Input data requirements for the conterminous US, Mexico, and Canada for 1980 - 2008 have been used to produce the Daymet product for these areas; the data will be released in Fallmore » 2010. MAST-DC is developing several ways to select and distribute the Daymet data: ftp download, single-pixel extraction, and access through THREDDS (Thematic Real-time Environmental Distributed Data Services) Data Server (TDS). Periodic updates to the continental data set will be implemented as new years of surface observations become available.« less
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".