NCAR/GPEP: Version 1.0.0-alpha release
Bibliographic record
Abstract
The first release of the GPEP package Geospatial Probabilistic Estimation Package (GPEP) is a python-based tool for generating gridded analyses of time-varying geophysical variables based on merging point/in-situ and spatially-distributed (i.e., gridded) observations and predictor variables. It was developed to expand on and advance the capabilities of the Gridded Meteorological Ensemble Tool (GMET: https://github.com/NCAR/GMET), which is written in FORTRAN. GPEP reproduces the baseline GMET capabilities (see Bunn et al, 2022) that were developed largely for meteorological dataset generation in climate and water resources applications, including creating inputs for hydrologic simulation and prediction. GPEP has a more flexible structure and provides a much broader array of methods than GMET, which relied solely on locally-weighted spatial linear and logistic regression methods. GPEP also has certain technical differences which were either unavoidable or pragmatic due to the conversion from FORTRAN to Python (including a different approach to cross-validation). Sponsorship US Army Corps of Engineers -- Climate Preparedness and Resilience Program (NCAR) US Bureau of Reclamation -- Snow Water Supply Forecasting Program (NCAR) Global Water Futures (U. Saskatchewan) Reference: Tang, G., Wood, A. W., Newman, A. J., Clark, M. P., and Papalexiou, S. M.: GPEP v1.0: a Geospatial Probabilistic Estimation Package to support Earth Science applications, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-172, in review, 2023.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.043 |
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".