River channel's predisposition to ice jams: a geospatial model
Bibliographic record
Abstract
When dynamic breakup occurs on rivers, ice moving downstream may eventually stop at an obstacle when the volume of moving ice exceeds the transport capacity of the river, resulting into an ice jam. The suddenness and unpredictability of these ice jams are a constant danger to local population. Therefore forecasting methods are necessary to provide an early warning to these population. Nonetheless the morphological and hydrological factors controlling where and how the ice will jam are numerous and complex. Existing studies which exist on this topic are highly site specific. Therefore, the goal of this work is to develop a simplified geospatial model that would estimate the predisposition of any river channel to ice jams. The question here is not to predict when the ice will break up but rather to know where the released ice would be susceptible to jam. This paper presents the developments and preliminary results of the proposed approach. The initial step was to document the main factors identified in the literature, as potential cause for an ice jam. First, several main factors identified in the literature as potential cause for an ice jam have been selected: presence of an island, narrowing of the channel, sinuosity, presence of a bridge, confluence of rivers and slope break. The second step was to spatially represent, in 2D, the physical characteristics of the channel and to translate these characteristics into potential ice jamming factors. The Chaudiere River, south of Quebec City (Canada), was chosen as a test site. Tools from the GIS-based FRAZIL system have been used to generate these factors from readily available geospatial data and calcutate an “ice jam predisposition index” over regular-spaced segments along the entire channel. The resulting map was validated upon historical observations and local knowledge, collected in relationship with the Minister of Public Security.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".