Event-Scale Hydrologic Response in Urbanizing Watersheds of the Canadian Great Lakes Basin and Associations with Fish Richness
Bibliographic record
Abstract
The cumulative impacts of urban land use on stream flow regimes and lotic ecosystems are poorly understood.Moreover, flow assessments using daily or monthly flows cannot adequately characterize event-scale flow dynamics in urbanizing watersheds.Accordingly, this empirical research examined high temporal resolution (15-minute) growing season hydrologic records in the Greater Toronto Region, Canada.Hydrologic records were matched with rainfall records to include precipitation in models.The first phase of research identified temporal trends in total runoff, rising limb event flows and rising limb accelerations in two watersheds.Results indicated dramatic changes: over a 42-year period, total seasonal discharge increased 45% in the Don and Humber Rivers during a period of stable rainfall patterns.Peak event flows and event flow variability also increased temporally.The second phase of research comprised a spatial analysis of twenty-seven watersheds ranging from 38 km 2 to 806 km 2 undertaken along an urban land use gradient from less than 0.1% to 88%.Urban land use had a very strong influence on total runoff and event scale runoff.Changes in runoff characteristics began at urban cover under 4%.Event flow acceleration increased, causing maximum runoff to be reached sooner as urban cover increased.The total runoff model had an interaction between watershed size and urban land use.The third phase of research identified associations of fish species richness with event-scale hydrologic characteristics in eight watersheds using fish data spanning approximately five decades.Maximum event flow acceleration and skew in instantaneous runoff explained a higher proportion of variation than urban percent in empirical models.Historic fish data are difficult to obtain and pose analytical challenges.By using high temporal resolution flow data, the research provides xiv
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".