Bibliographic record
Abstract
Quality characteristics for 21586 river flow time series from 13 datasets worldwide. The 13 datasets are: the Global Runoff Database from the Global Runoff Data Center (GRDC), the Global River Discharge Data (RIVDIS; Vörösmarty et al., 1998), Surface-Water Data from the United States Geological Survey (USGS), HYDAT from the Water Survey of Canada (WSC), WISKI from the Swedish Meteorological and Hydrological Institute (SMHI), Hidroweb from the Brazilian National Water Agency (ANA), National data from the Australian Bureau of Meteorology (BOM), Spanish river flow data from the Ecological Transition Ministry (Spain), R-ArcticNet v. 4.0 from the Pan-Arctic Project Consortium (R-ArcticNet), Russian River data (NCAR-UCAR; Bodo, 2000), Chinese river flow data from the China Hydrology Data Project (CHDP; Henck et al., 2010, 2011), the European Water Archive from GRDC - EURO-FRIEND-Water (EWA), and the GEWEX Asian Monsoon Experiment (GAME) – Tropics dataset provided by the Royal Irrigation Department of Thailand. Quality characteristics are based on availability, outliers, homogeneity and trends: overall availability (%), longest availability (%), continuity (%), monthly availability (%), outliers ratio (%), homogeneity of annual flows (number of statistical tests agreeing), trend in annual flows, trend in one month of the year. Bodo, B. (2000) Russian River Flow Data by Bodo. Boulder CO: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Retrieved from http://rda.ucar.edu/datasets/ds553.1/ Henck, A. C., Huntington, K. W., Stone, J. O., Montgomery, D. R. & Hallet, B. (2011) Spatial controls on erosion in the Three Rivers Region, southeastern Tibet and southwestern China. Earth and Planetary Science Letters 303(1–2), 71–83. doi:10.1016/j.epsl.2010.12.038 Henck, A. C., Montgomery, David R., Huntington, K. W. & Liang, C. (2010) Monsoon control of effective discharge, Yunnan and Tibet. Geology 38(11), 975–978. doi:10.1130/G31444.1 Vörösmarty, C. J., Fekete, B. M. & Tucker, B. A. (1998) Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS). doi:10.3334/ornldaac/199
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".