Quantifying the streamflow regime of Hanlon Creek, Ontario to develop streamflow management targets
Bibliographic record
Abstract
Maintaining the natural hydrologic variability of streamflows is critical for conserving river ecosystems. The hydrological consequences of gradual urbanization between reference (1950s) and existing (2001) conditions in the Hanlon Creek watershed were investigated. The effects of stormwater management/recharge ponds and natural conservation areas on the streamflow regime were also considered. A calibrated hydrologic (GAWSER) model of the Hanlon Creek watershed was applied, using long term continuous rainfall data, to generate a daily streamflow time series at eight gauged and ungauged points of interests, under different land use scenarios. A flow assessment tool "Indicators of Hydrologic Alteration (IHA)" utilized in GAWSER and IHA software was demonstrated to characterize the natural and altered flow regime of the study area in terms of 34 ecologically related hydrologic parameters. The "Range of Variability Approach (RVA)" included in the flow assessment tool was also applied to assess the degree of flow alteration and to set initial river management targets to restore a near-natural flow regime. The study demonstrates the suitability of the suite of tools (GAWSER, IHA and RVA) for simulating daily flows; characterizing the flow regime (in terms of the magnitude, frequency, duration and timing of flows); quantifying the alteration of the flow regime due to urbanization; and setting flow targets for an urbanized watershed, based on a GAWSER generated daily streamflow time series. A methodology has been developed for applying and adapting these tools, which may be used in other watersheds.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".