Remote sensing technology utilized by CHS to support safety of navigation in the Arctic
Bibliographic record
Abstract
To enhance navigational safety, the Canadian Hydrographic Service (CHS) is investing in data collection to improve its hydrographic survey coverage and the necessary data needed for producing nautical products. Over the past eight years, the CHS has accelerated its efforts under the Oceans Protection Plan (OPP) initiative. For the CHS, the main goal is to bolster safety of navigation in Canada's waterways by providing better products and addressing some of our client’s needs, like the northern communities. This initiative not only accelerated the collection of hydrographic surveys but also incorporated innovative technologies such as remote sensing. These advancements are important to the CHS operations, as they support survey missions and facilitate the development of new remote sensing derived products that contribute to the creation of nautical products. Despite considerable progress under the OPP in expanding survey coverage, just 18% of Canadian Arctic waters are currently surveyed to modern standards. To help address these gaps, remote sensing technology is being employed to extract various forms of information. Among the common derived products are shoreline, intertidal zone, navigation hazard, satellite-derived bathymetry (SDB), and change detection for monitoring dynamic areas. By utilizing remote sensing data, the CHS is developing a new workflow designed to tackle challenges posed by rapidly changing environments. A site on the Mackenzie River in Canada was selected for its dynamic nature to test this workflow and its potential as an improved chart creation model in dynamic environments. Additionally, the CHS is testing the integration of space-based altimeter systems such as SWOT and ICESat-2, which provide precise water level and depth information, into existing workflows to reduce costs and capitalize on increased data availability in remote areas. The ongoing efforts of the CHS reflect a commitment to improving navigational safety through innovative solutions, ensuring that navigators have access to the most accurate and up-to-date products.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".