Addressing Chronic Flooding in a Dynamic River System through an Ice Management Plan
Bibliographic record
Abstract
The Lower Ausable River discharges into Lake Huron near the hamlet of Port Franks, south of Grand Bend, Ontario.Port Franks has a long history of icerelated flooding problems.The Ausable Bayfield Conservation Authority (ABCA) has conducted several hydraulic studies of the Lower Ausable River in the past to identify critical ice jam formation areas and recommend measures to minimize flooding hazards to properties along the river.The ABCA has recently undertaken an update of these studies, covering a 9.2 km (5.7 mi) reach that was originally channelized for drainage purposes over 130 years ago.The objective of the study was to identify river sections most susceptible to the formation of ice jams through hydraulic modeling, and to recommend a set of mitigative measures that address the resultant flooding.Key project challenges have included the economy of data collection methodologies (e.g., the use of digital bathymetric soundings and land terrain models, augmented with new GPS survey) as well as addressing new environmental permitting requirements.This chapter presents a summary of the ice management study procedures and findings, including a summary of the theory of ice jam formation, hydraulic modeling methodologies, the identification and prioritization of susceptible ice jam locations, and an overview of mitigative measures (that is, structural and operational controls) to minimize ice jam potential.A key highlight of this
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".