Investigation of the occurrence of ice jams on the Lower Red River in Manitoba
Bibliographic record
Abstract
The Lower Red River in Manitoba regularly experiences ice jam flooding, with the most severe events occurring between Lockport and Netley Lake. This research investigates the timing and frequency of ice jamming on this section of the Lower Red River, the relationship between ice jams and antecedent conditions, and different ice jam prediction methods and their suitability for the study area. By gaining a better understanding of ice jamming trends in the area, this research provides accessible prediction methods that can help guide decisions related to the risk and severity of spring ice jamming. A database of ice jam events was developed with each event given a severity rating from 1-5, based on the resulting flood from the ice jam. Out of 54 ice jam events from 1962-2017, the most common event locations were found to be Sugar Island, Selkirk Bridge, and the Netley Creek Confluence. All ice jam events occurred when the peak spring flow exceeded 1000 cms and all severe events (severity 3+) occurred when peak spring flows exceeded 1500 cms. Three different ice jam prediction models including a threshold model, regression model, and discriminate function analysis (DFA) were developed using meteorological and hydrometric parameter data. The threshold model proved to be the best tool to predict severe events, as its predictions differentiated all severe event years from non-event years with only one false positive result. The quadratic three-outcome DFA had success in predicting minor ice jam years (severity 1-2) and differentiating them from severe ice jam or no ice jam years and is therefore recommended to use alongside the threshold model. The regression model was not as effective as the threshold model or DFA in predicting ice jamming.
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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) | 0.001 | 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".