Modelling ice formation in the regulated river Orkla with River 1D
Bibliographic record
Abstract
Abstract The Orkla River, located in Norway, is a important river system known for its diverse hydrological characteristics and significant ecological value. Situated in the central part of the country, the Orkla River flows through the counties of Trøndelag and Innlandet. It spans approximately 179 kilometres, originating from the high mountain areas around Orkelsjøen and flows into the Trondheimsfjord. The river was regulated for hydropower in the 1980s and hydropower operation has changed the seasonality of flow and water temperature. With the current regulation, we have several hydropower outlets and a river intake on the main river in the Orkla valley, creating a variable hydrological regime in the river that differs from the natural winter conditions. River ice dynamics play a crucial role in the hydrological and ecological processes of cold regions, impacting water flow, flood risk, and habitat availability. Regulation for hydropower is known to influence river ice and this is also the case in Orkla, having effectsboth on the physical conditions in the river and on the operation of the hydropower plant. This paper presents a study on modelling ice in the Orkla River using the University of Alberta’s River 1D ice model. This paper describes the setup of the model and how it is adapted to the winter conditions in river Orkla on the reach between the outlet of the Grana power plant and the intake to Svorkmo power plant. The model is calibrated and validated using observed data, including observed drifting and temperature and discharge measurements to ensure its accuracy in simulating the ice dynamics specific to the Orkla River.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".