CGU HS Committee on River Ice Processes and the Environment 14th Workshop on the Hydraulics of Ice Covered Rivers
Bibliographic record
Abstract
The purpose of the FRAZIL Project is to develop a GIS-based system in support of winter river flow modelling and ice-related flood forecasting. Usually, in situ measurements and simulation models are used to characterize the state of the river and to foresee its future behaviour. The advantage of using GIS in hydraulic modelling is the potential for extracting topographically correct cross-section data from a DTM that can be used to determine river stage and floodplain extent as calculated in hydraulic modelling software package. However, such applications require a detailed description of the channel geometry and do not deal specifically with winter flow modelling and the presence of an ice cover. River1D, a one-dimensional unsteady flood routing model has been developed to study ice-related events and has been successfully used to adequately predict flood hydrographs over long river reaches, based on relatively limited data. The FRAZIL GIS-based system is well adapted to provide some of the physical characteristics of the river channel. It proposes tools to assist in building the channel geometry, partitioning the river and preparing data for the hydraulic model, while taking advantage of the database and other functions of the ArcGIS software. It is also developed to take advantage of the river ice information which can be derived from a radar image. This component provides an ice map, ice coverage for reaches along the river, relative ice roughness and the location and length of ice jams. This paper presents the approaches used in the development of the FRAZIL tools for River1D. The demonstration is made on a section of the Athabasca River (Alberta).
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".