The geomorphic sensitivity of rivers in the Spencer Creek watershed and its implication for watershed management in Hamilton, Ontario
Bibliographic record
Abstract
The geomorphic sensitivity of rivers in the Hamilton area to land-use and climate change has not been previously examined, despite its implications for watershed management. We use three stream power-based approaches to assess the geomorphic sensitivity of rivers within the Spencer Creek watershed: (1) network-scale maps of total stream power and its changes under rural, current land-use and future climate change scenarios, (2) an equilibrium width comparison at sample reaches, and (3) a threshold grain size analysis at sample reaches. The highest total stream power for all scenarios occurred along the Niagara Escarpment, as well as through neighborhoods with high slopes and intense urbanization. The increase of total stream power between rural and current land-use is highest in the southern section of the watershed, and the future climate-change scenario indicated an amplification of these spatial patterns. The equilibrium width and threshold grain size approaches categorized most sampled reaches as sensitive, indicating a potential for erosion and geomorphic adjustment. A lack of spatial pattern among the sensitivity of sampled reaches suggests that reach-scale analyses better capture localized conditions. Examples of how stream power-based analyses at the network and reach scales can inform river monitoring and watershed management decisions are discussed.
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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".