Snow Drought Risk and Susceptibility - Western United States and Southwestern Canada
Bibliographic record
Abstract
These five files are rasters of snow drought risk (3 files) and susceptibility (2 files) over the mountain and inter-mountain regions of western United States and southwestern Canada. The snow drought risk rasters (risk_dry.asc, risk_warm.asc, risk_warm_dry.asc) correspond to the dry snow drought risk, warm snow drought risk, and warm and dry snow drought risk. Risk is calculated as the mean severity (fraction below long-term [1951-2000] peak snow water equivalent [SWE] mean) multiplied by the frequency (fraction of total years [n = 63]). Thus, snow drought risk in each raster has units of fractional deficit per year and is equal to the expected annual deficit in peak SWE for each snow drought type. The susceptibility rasters (susceptibility.asc, susceptibility_plus2degC.asc) contain the categorical ranking of temperature-related snow drought susceptibility over the mountain and inter-mountain western United States and southwestern Canada. The susceptibility.asc file represents the historical susceptibility (1951-2000) and the susceptibility_plus2degC.asc file represents the susceptibility under 2 degrees of warming (relative to 1951-2000). Raster values correspond to susceptibility rankings as follows: 0 = negligible, 1 = low, 2 = medium, 3 = high. All rasters are in ESRI Ascii (.asc) format and were created with the "raster" package in R. Resolution is 1/16 degree. Extent: xmin = -125; xmax = -100; ymin = 30; ymax = 53. For further details, see: Dierauer, J.R., Allen, D.M., & Whitfield, P.H. Snow drought risk and susceptibility in the western United States and southwestern Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".