The MET Norway Ice Service: a comprehensive review of the historical and future evolution, ice chart creation, and end user interaction within METAREA XIX
Bibliographic record
Abstract
The MET Norway Ice Service (NIS) celebrated its fiftieth year as a formal operational sea ice information provider in 2020. Prior to the 1970’s, support to navigation had started off with ad-hoc observations from coastal stations on Svalbard in the 1930’s, before developing as a research programme in the 1960’s. Activity in the region has steadily increased, and now the NIS also supports a large number of research, tourist, and resource exploration vessels, in addition to the ice chart archive being a resource for climate change research. The Ice Service has always been at the forefront in the use of satellite Earth Observation technologies, beginning with the routine use of optical thermal infrared imagery from NASA TIROS and becoming a large user of Canadian RADARSAT-2 Synthetic Aperture Radar (SAR), and then European Copernicus Sentinel-1, in the 2000’s and 2010’s. Initially ice charts were a weekly compilation of ice information using cloud-free satellite coverage, aerial reconnaissance, and in situ observations, drawn on paper at the offices of the Norwegian Meteorological Institute (MET Norway) in Oslo. From 1997 production moved to the Tromsø office using computer-based Geographical Information System (GIS) software and the NIS developed the ice charting system Bifrost . This allowed the frequency of production to be increased to every weekday, with a greater focus on detailed sea ice concentrations along the ice edge and coastal zones in Eastern Greenland and in the Svalbard fjords. From 2010, the NIS has also provided a weekly austral summer ice chart for the Weddell Sea and Antarctic Peninsula. To further develop its capabilities, NIS engages in a number of national and international research projects and led the EU Horizon 2020 project, Key Environmental monitoring for Polar Latitudes and European Readiness (KEPLER). This paper summarises the overall mandate and history of the NIS, and its current activities including the current state of routine production of operational ice charts at the NIS for maritime safety in both the Arctic and Antarctic, and future development plans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".