Development of an ice jam database and prediction tool for the Lower Red River
Bibliographic record
Abstract
The Lower Red River in Manitoba regularly experiences springtime ice jam flooding, with the most severe events occurring between Lockport and Netley Lake. A database of ice jam events was developed through newspaper archives and historical stage data. Each event was given a severity rating from 1-5, based on the resulting ice jam flood. This facilitated an investigation of ice jam timing and frequency on this section of the Lower Red River and the development of a threshold-based ice jam prediction tool. Out of 54 ice jam events from 1962-2017, 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. The threshold ice jam prediction model was developed using five meteorological and hydrometric parameters including accumulated degree day of thaw, accumulated degree day of freezing, freeze-up water level, snow on ground, and rain equivalence. The model was able to differentiate all severe event years from non-event years with only one false positive result. By gaining a better understanding of ice jamming in the area, this research provides an accessible prediction method that can help guide decisions related to the risk and severity of spring 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".