Real-Time Measurement of Ice Draft and Velocity in the St. Lawrence
Bibliographic record
Abstract
responsibilities to prevent and break ice jams in order to minimize the risks of flooding and maintain safe navigation conditions on the St. Lawrence River throughout the winter months. Near real-time information about the coverage, thickness and motion of the ice cover in the navigation channel are required to coordinate icebreaking for maintaining the shipping route, and to prevent and identify ice jams as they develop. Aerial and satellite surveillance provides ice coverage data, but not thickness. This paper describes a test installation in the St. Lawrence that provides real-time ice thickness, ice motion, current velocity and meteorological data from a remote site. The IPS (Ice Profiling Sonar) and ADCP Data Display System (IADDS) consists of two submerged instruments (IPS and ADCP), connected by cable to a nearby lighthouse that is equipped with a computer, weather station, appropriate display software and data transmission capability to shore and the Fisheries and Oceans Department s network. The principles and operation of the IPS and the use of an ADCP to measure ice velocity are described. The IPS and ADCP are installed at 13m depth in the navigation channel in Lac St. Pierre in the St. Lawrence River. Real-time data from the instruments and the weather station are collected at the lighthouse site, and then formatted and transmitted to the Coast Guard headquarters in Quebec City, approximately 200 km away. Web-compatible graphs of the data are then produced for display on the Coast Guard Intranet. The structure of the control, data transmission and storage software is described, and examples are given of the data and its use for managing navigation and detecting ice jams. The results of on-site validation measurements made in the winter of 2002-2003 are also described. I.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".