Geospatial modelling to determine the behaviour of ice cover formation during freeze-up of the Dauphin River in Manitoba
Bibliographic record
Abstract
During the formation of a frazil-generated ice cover, ice bridging can occur along sections of rivers that are geomorphologically suited (e.g. very sinuous, narrowing channel width, low gradient bed slope) to arrest the flow of ice pans. Bridging may occur at several locations along the river independently from one another with separate ice cover formations occurring simultaneously. Juxtaposition of incoming frazil ice and ice pans extending from the ice bridgings can cause backwater levels to rise upstream and cause flooding. The separate ice covers will eventually merge to completely cover the river sooner than if no additional bridging had occurred. This makes it difficult to predict the time required for a river to be completely ice covered and the locations and timing of related potential flood events. A geospatial model is introduced in this paper that applies a principal component analysis to cluster geomorphological features – such as sinuosity, channel width and channel slope – into typologies. Certain combinations of these typologies reveal predisposition of certain geomorphological features to ice bridging or non-bridging. The Dauphin River in Manitoba is used as a test case for the development of the model.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".