A geospatial model to determine patterns in river ice cover breakup and jamming behaviour
Bibliographic record
Abstract
In the past, both empirical and process-based attempts have been made to predict river ice behaviour, in particular ice cover breakup and ice jamming occurrences. These methods perform with varying and limited success and tend to be site specific. A method is required which can simply estimate the predisposition of river reaches to ice breakup and jamming events. This paper introduces a geospatial modelling approach which can fulfil that task and improve the predictive power of ice cover breakup and ice jamming behaviour. The geospatial model can determine the most vulnerable sections along the studied reaches to such behaviour, which are phenomena entailing hydraulic, ice morphology and fluvial geomorphology. A geospatial model clusters hydraulic characteristics (e.g. discharge or stage), ice characteristics (e.g. ice thickness \nand ice type) and river geomorphological characteristics (e.g. sinuosity, slope, width, etc.) into common river features called Geomorphic Response Units (GRU). A statistical clustering technique such as principle component analysis (PCA) is used to derive these GRUs. It is assumed that certain GRUs will be more susceptible to certain ice cover behaviour, such as breakup and jamming of river ice. Data acquired along the Slave River and its delta in Canada is used to test the geospatial model. The main data sources are space-borne remote sensing MODIS imagery and traditional and local knowledge from members of the communities alongside the river, in particular Fort Resolution and Fort Smith
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".