GLOBal river SALiniTy and associated ions (GlobSalt)
Bibliographic record
Abstract
Abstract Freshwater salinization (FS) is a threat to freshwater ecosystems, but its impact remains relatively poorly understood compared to other stressors (e.g. nutrient pollution), with some regions (e.g. Asia, Africa) remaining poorly explored. To assess how pervasive this issue is globally and identify salinization hotspots, we compiled global data on river salinity and associated ions. We retrieved information from different sources, harmonized it and merged it with HydroATLAS watersheds. Our global data set (GlobSalt) features 13 parameters, including electrical conductivity (EC), major ions, and nutrients. GlobSalt contains approximately fifteen million records on a monthly scale for river stations from 1980 to 2023 from all continents except Antarctica. The global median EC was 509 ± 205 μS cm −1 , with 60% of rivers falling in the range of 50 to 500 μS cm −1 , which is within the salinity niche of most freshwater organisms. We found a large spatial variability in EC, with some regions such as the Mediterranean, the Midwest of the US, arid regions of Argentina and Chile and Southwestern Australia having high mean salinity values. Temporally, EC was fairly stable. GlobSalt represents a critical resource for improving our understanding of FS dynamics, identifying regions at high risk and informing management strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".