Sustained Observations of Changing Arctic Coastal and Marine Environments and Their Potential Contribution to Arctic Maritime Domain Awareness: A Case Study in Northern Alaska
Bibliographic record
Abstract
Increased maritime activities and rapid environmental change pose significant hazards, both natural and technological, to Arctic maritime operators and coastal communities. Currently, U.S. and foreign research activities account for more than half of the sustained hazard-relevant observations in the U.S. maritime Arctic, but hazard assessment and emergency response are hampered by a lack of dedicated hazard monitoring installations in the Arctic. In the present study, we consider a number of different sustained environmental observations associated with research into atmosphere-ice-ocean processes, and discuss how they can help support the toolkit of emergency responders. Building on a case study at Utqiaġvik (Barrow), Alaska, we investigate potential hazards in the seasonally ice-covered coastal zone. Guided by recent incidents requiring emergency response, we analyze data from coastal radar and other observing assets, such as an ice mass balance site and oceanographic moorings, in order to outline a framework for coastal maritime hazard assessments that builds on diverse observing systems infrastructure. This approach links Arctic system science research to operational information needs in the context of the development of a Common Operational Picture (COP) for Maritime Domain Awareness (MDA) relevant for Arctic coastal and offshore regions. A COP in these regions needs to consider threats not typically part of the classic MDA framework, including sea ice or slow-onset hazards. An environmental security and MDA testbed is proposed for northern Alaska, building on research and community assets to help guide a hybrid research-operational framework that supports effective emergency response in Arctic regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".