From river flow regime diversity to proxies for hydrologic homogeneity a Canada-wide case study
Bibliographic record
Abstract
Zonal classifications, such as those based on biomes and ecozones, are commonly used to contextualize short-term dynamics and long-term environmental change. One challenge in hydrology is the lack of zonal classifications that explicitly incorporate flow statistics. To date, few studies have evaluated whether non-hydrological zonal classifications can serve as proxies for flow dynamics across large, heterogenous regions. Taking Canada as an example, the focus was on 2531 hydrometric stations for which select streamflow signatures were computed. Those signatures, coupled with catchment characteristics, were used to distinguish flow regimes based on their degree of temporal variability-categorizing them as erratic or persistent-and their main water sources-either shallow subsurface flow or groundwater. Results show that catchments with higher cropland and urban cover and higher percentages of clay soils were associated with erratic regimes fed by shallow subsurface flow. Conversely, catchments with higher forest and semi-permanent water features were associated with persistent regimes. The high degree of intra-region and inter-region hydrologic heterogeneity was typically not well captured by non-hydrological zonal classifications. Caution is therefore warranted when using existing non-hydrological zonal classifications for regional water policy planning, as they may lead to a mischaracterization of spatial differences in streamflow patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".