A multidisciplinary framework to derive global river reach classifications at high spatial resolution
Bibliographic record
Abstract
Projected climate and environmental change are expected to increase the pressure on global freshwater resources.To prepare for and cope with the related risks, stakeholders need to devise plans for sustainable management of river systems, which in turn requires the identification of management-appropriate operational units, such as groups of rivers that share similar environmental and biological characteristics.Ideally, these units are of a manageable size, and are biotically or abiotically distinguishable across a variety of river types.Here, we aim to address this need by presenting a new global river classification framework (GloRiC) to establish a common vocabulary and standardized approach to the development of globally comprehensive and integrated river classifications that can be tailored to different goals and requirements.We define the GloRiC conceptual framework based on five categories of variables: (1) hydrology; (2) physiography and climate; (3) fluvial geomorphology; (4) water chemistry; and (5) aquatic biology.We then apply the framework using hydroenvironmental attributes provided by a seamless high-resolution river reach database to create initial instances of three sub-classifications (hydrologic, physio-climatic, and geomorphic) which we ultimately combine into 127 river reach types at the global scale.These supervised classifications utilize a mix of statistical analyses and expert interpretation to identify the classifier variables, the number of classes, and their thresholds.In addition, we also present an unsupervised, multivariable k-means statistical clustering of all river reaches into 30 groups.These first-of-their-kind global river reach classifications at high spatial resolution provide baseline information for a total of 35.9 million kilometers of rivers that have been assessed in this study, and are expected to be particularly useful in remote or data-poor river basins.The GloRiC framework and associated data are primarily designed for broad and rapid applicability in assessments that require stratified analyses of river ecosystem conditions at global and regional scales; smaller-scale applications could follow the same conceptual framework yet use more detailed data sources.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".