Assessing channel geometry in response to land use disturbance in a low‐relief, glacially conditioned setting
Bibliographic record
Abstract
Abstract Bankfull stage, the highest flow elevation contained by a river channel before overbank flooding occurs, is the presumed threshold for channel morphological change. At bankfull, the channel boundary experiences the highest shear stress, producing somewhat predictable hydraulic relationships and a cross‐sectional form. However, land use and glacial conditioning can profoundly impact a channel's geomorphic responses. Two common methods characterize bankfull flow: one based on flow frequency and other based on channel form. This study considers a simplified approach to identify upstream land use relative to estimates of bankfull flow versus a channel's geometric form. The approach compares archived geomorphic surveys of 140 river reaches in southern Ontario, Canada, to 2‐year flood quantiles modelled from historical flood data of 207 gauge stations. Flood frequency analysis determines that annual maximum series (AMS) datasets, fitted to optimized probabilistic distributions, underestimate discharge for low‐magnitude, high‐frequency flood events compared to partial duration series (PDS) datasets. For smaller drainage areas (<100 km2) associated with an extensive agricultural activity and/or urbanization, the estimates of bankfull discharge (Qbf) generated by cross‐sectional channel geometry are greater than the gauge‐derived Q2 values. Channels impacted by high levels of upstream land use disturbance show statistically significant lower width‐to‐depth ratios (p < 0.001) and a trend towards a finer D50 bed material, suggesting enhanced surface runoff delivering more mobile finer sediments and channel confinement or incision. This research quantifies the formative bankfull stage to better understand the link between land use and a channel's ‘natural’ hydrogeomorphic response in a low‐relief, glacially conditioned setting.
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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 | 0.000 | 0.001 |
| 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".