Associations of event‐scale flow hydrology with fish richness in urbanizing Canadian watersheds of Lake Ontario
Bibliographic record
Abstract
Abstract Urbanization is associated with declines in aquatic biodiversity and changes to flow regimes. This empirical research examined high temporal resolution (15 min) hydrologic records and associations with fish species richness in eight river systems in the Toronto region, Canada. The dataset spanned approximately five decades and covered the annual post‐freshet period to mid‐November. The high‐temporal resolution flow records allowed estimation of flow acceleration (a measure of the rate of change in flow) in response to rain events. Maximum rising limb event flow acceleration and skew in instantaneous runoff explained a higher proportion of variation than percent urban land use in empirical models with long‐term fish records. Models fit using only the most recent decade of records did not produce the same results, likely indicating that analyses of flow with fish diversity require sufficient range in flow conditions for the statistical signals to be detected. Historic fish data are difficult to obtain and pose analytical challenges due to bias and inconsistent collection methods. Despite the data limitations, the study results point to the need for more research into potential causal factors contributing to negative fish richness in urbanizing watercourses with periods of high flow acceleration.
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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 | 0.001 | 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".