Characteristics and distribution of natural flow regimes in Canada: a habitat template approach
Bibliographic record
Abstract
Extremes of flow and patterns of flow variability limit the distribution and abundance of riverine species via a natural disturbance regime. Using a habitat template approach, we describe the distribution and characteristics of natural flow regimes in Canada based on the severity of flows, flow predictability, and flow variability. Bayesian clustering was used to group 888 gauged watersheds across Canada into 10 classes. Some flow classes were found in all provinces, whereas others showed greater regional grouping related to land physiography (e.g., Canadian Shield and ecozones). Ontario and British Columbia had the greatest diversity of flow classes. Larger river systems tended towards less harsh flow regimes and greater flow regularity than small systems. A stream–lake network pattern, particularly the presence of lakes, decreased the severity of flow. The flow metric flood-free interval was found to be a potentially misleading indicator of reduced disturbance for high-latitude streams in Canada where ice formation and persistence are important stress factors for biota. Most flow stations had an 80% or higher chance of belonging to their primary membership class. Quantifying uncertainty in class assignment can help fellow scientists and resource managers appropriately apply our findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".