ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins
Bibliographic record
Abstract
This is the readme file for the ET-WB dataset described in the ESSD paper "ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins" from Xiong et al. (2023) ET-WB dataset-The monthly water balance data from May 2002-December 2021 for the 168 river basins and global land from 23 precipitation, 29 runoff, and 7 terrestrial water storage changes datasets. The five dimensions (236*169*23*7*29) of the matrix represent the time, regions, precipitation, terrestrial water storage changes, and runoff datasets used respectively. ET-WB is distributed in three kind of formats: Mat (ET-WB.mat), NetCDF (ET_WB.nc), and Shapefile (ET_WB.shp) (only for the ensemble median value). All the formats share the same definitions of dimensions (as below), except for the ArcGIS shapefile that is provided for individual regions (168 river basins and global land excluding Antarctic and Greenland). File shapefile.rar is the geospatial database of the study area that can be opened in ArcGIS software. Please find more details in the Readme file.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.036 |
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".