Multidisciplinary classification of Canadian river reaches to support the sustainable management of freshwater systems
Bibliographic record
Abstract
Climate change is altering the hydrology of Canadian rivers to an unprecedented degree. Consequently, sustainable freshwater management practices need to adapt, which include the development of integrated water resource strategies and environmental flow recommendations. A particular challenge arises for these assessments due to the highly diverse nature of river ecosystems across Canada. This diversity can be addressed by using a classification system to create manageable spatial units. Building upon and adapting the results of a global river reach classification scheme, we present a multidisciplinary K-means clustering approach to categorize all river reaches of Canada into 23 types at fine spatial resolution. The typology is based on classifier variables related to hydrology, mostly derived from modelled long-term means of monthly discharges, physiography, climate, and fluvial geomorphology. Resulting maps provide baseline information for rivers of all size classes, from small headwater streams to very large rivers, across the entire country, including unmonitored regions such as the High Arctic. The classification system also points to some strategic expansion opportunities for the current Canadian monitoring network of river gauging stations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".