Bibliographic record
Abstract
Introduction Many models have been created around the world over the last four decades or so to describe snowmelt runoff. Some 18 different models are listed and summarized in the Snow Hydrology Guide (Ontario Ministry of Natural Resources 1989). The World Meteorological Organization (1986) also lists and summarizes 18 different snowmelt runoff models. These many models are listed here in Table F-1. a. This section will focus on describing six snowmelt models that have been demonstrated as valuable operational models or are thought to have a high potential for future operational use by the U.S. Army Corps of Engineers. These models are as follows: . The SSARR Model (the Streamflow Regulation and Reservoir Regulation Model). . The HEC-1 and HEC-1F Models (the Hydrologic Engineering Center - 1, 1F Model). . The NWSRFS Model (the National Weather Service River Forecast System Model). . The PRMS Model (the Precipitation Runoff Modeling System Model) . The SRM (the Snowmelt Runoff Mo
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.786 | 0.462 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".