Data-guided exploration of streamflow generation mechanism: A global-scale analysis
Bibliographic record
Abstract
Identifying a catchment’s streamflow generation mechanisms could inform the hydrologic functioning of the catchment, and how the catchment responds to the changes in climate and land-use. This study focuses on identifying the dominant streamflow generation mechanism and its drivers at more than 2,000 natural catchments located in North and South America, Europe, and Oceania. First, in a given catchment, we use a suite of diagnostic tools to infer the relative contribution of different streamflow generation mechanisms from precipitation and streamflow observations and simulated time series of subsurface storage. Then, in a large sample hydrology framework, we explore the major physical and climatic drivers of streamflow generation mechanisms. In this study, we made progress in differentiation among, seemingly similar, but naturally different subsurface mechanisms of streamflow generation (e.g., subsurface stormflow, transmissivity feedback, groundwater flow) as well as in identifying the drivers of these mechanisms. Our study extracts generalizable process understanding by combining conventional hydrologic science tools with modern data learning techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".