Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities
Bibliographic record
Abstract
Sea ice charts produced by the ice services of the world are among the most widely used sources of information about sea ice conditions in the Arctic. The absolute majority of sea ice charts are based on visual expert analysis of satellite imagery accompanied by auxiliary data including ground information from coastal stations and ships navigating the Northern Sea Route (NSR). Ground measurements of sea ice thickness are necessary for validating the results of satellite imagery interpretation. Shipboard observations are highly valuable because, unlike coastal stations, the ships provide information about sea ice cover straight on the navigational routes, not in the coastal areas of land-fast ice, where the thickness values are not fully representative of the ice in the open sea. However, the current system of shipboard observations used by commercial fleets often does not meet the reliability requirements due to the human factor involved in the process of data collection. In the early 2000s, the Arctic and Antarctic Research Institute (AARI) suggested a new methodology for shipboard ice thickness measurement. A ship-based television complex (STC) was developed in order to exclude the human factor and standardize observations. The inaccuracy value was estimated as 3.8 % of the real thickness. By 2018, STC had been upgraded to a new ship-based television meteorological complex (STMC) allowing continuous automatic measurement of ice thickness and many other related hydrometeorological parameters during the entire voyage. The automatic and autonomous operation of the new equipment allows placing it on board the ship without the need for an ice specialist to be permanently present. It means that STMC can be used by commercial fleets, which constantly increase the number of Arc7 ice class vessels they use. For economic reasons, reinforced ice class vessels, whose number is growing, represent the only available infrastructure suitable for the deployment of distributed network providing operational hydrometeorological monitoring on the NSR. A comparison of STC data with AARI ice charts has revealed that real-time transmission of STC data from ships to the ice service office could increase the accuracy of ice charts and, as a consequence, the quality of the entire system of hydrometeorological informational support of maritime activities in the Arctic.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".