Operational monitoring of river ice on the Churchill River, Labrador
Bibliographic record
Abstract
Satellite synthetic aperture radar (SAR) has been used to support river ice modeling and flood forecasting as part of an operational river ice service for the Churchill River since 2008. Satellite imagery collection started prior to the construction of the Muskrat Falls Hydroelectric dam to establish a baseline of ice conditions and continued throughout the construction and subsequent operations. SAR imagery acquired using a wide range of incidence angles are used in concert with optical satellite images, webcams, local knowledge, weather forecasts and in-situ observations. SAR image analysis results in three products; ice cover, ice classification, and ice cover changes. The SAR classifications are frequently assessed by a local River Watch Committee who provide qualitative feedback on the classification accuracy. In 2017, triggered by a need to provide ice thickness measurements, a helicopter-borne ground penetrating radar (GPR) system was developed to determine a transect of ice thickness along the river. Beginning in 2019, four Sea Ice Mass Balance Array (SIMBA) buoys were adapted for deployment on the river. The SIMBA buoys measure and transmit, via Iridium, an ice temperature profile from which ice thickness can be derived. The buoys are deployed at four key locations on the river once the ice is thick enough to safely support installation. Manual ice thickness measurements are acquired opportunistically to validate the ice thickness measurements. The data collected are of critical importance for the Water Resources Management Division (WRMD) of the Government of Newfoundland and Labrador, and are used as inputs for river ice flood forecast models. The diverse temporal and spatial scales of data collection afforded by satellite SAR, airborne GPR and in-situ SIMBAs allow cost-effective surveillance of the river while minimizing risk to personnel. The data products are delivered through IceSight, C-CORE’s web-based platform, which offers additional analytics and facilitates inter- and intra-annual comparisons.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".