Daily snow water equivalent (SWE) observations from 1,065 stations in western North America for years 1960 - 2019.
Bibliographic record
Abstract
This data set includes daily snow water equivalent (SWE) observations for the historical record to September 2019 at 1,065 stations in the western U.S. and western Canada. Remote telemetry stations measure snowpack by relating measurements of the overlying snowpack mass to the water depth equivalent. The data were obtained from the Natural Resources Conservation Service, the California Department of Water Resources, Alberta Environment, the British Columbia Ministry of Environment, and the Yukon Government Water Resources Branch. Two levels of data are provided. The file snowPillowSWE_westernNA_level1_ncc.nc has been formatted but not quality controlled. The file snowPillowSWE_westernNA_level2_ncc.nc has been formatted and quality controlled. Both files are in netCDF format. The level 1 product provides daily SWE from January 1, 1960, to September 1, 2019 (21,794 days; row dimension) for 1,065 stations (column dimension). The level 2 SWE product is formatted by water year (October 1 to September 30) starting on Oct. 1, 1960 and ending on September 1, 2019, for the 1,065 stations and 59 years. Thus, it is of dimension [366 days,1065 stations, 59 years]. See the metadata in the netCDF files for information on all variables. Missing data are indicated by NaN. Please refer to the following publication for more information on the data and QA/QC methods: Musselman, K.N., N. Addor, J. Vano, and N.P. Molotch (2021). Winter melt rates portend widespread declines in snow water resources. Nature Climate Change. DOI: 10.1038/s41558-021-01014-9
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".