Innovative Real-Time Observing Capabilities for Remote Coastal Regions
Bibliographic record
Abstract
Remote regions across Alaska are challenging environments for obtaining real-time observations due to lack of power, easy road access and robust communication systems. The Alaska Ocean Observing System (AOOS) has partnered with federal and state agencies, local non-profits and universities to demonstrate innovative observing technologies and infrastructure support applications that address these challenges. These solutions are designed to meet observing needs for forecasting and reporting conditions for safe navigation, response to emergencies and coastal hazards, and are delivering real-time surface current, sea ice, water level and weather data in areas which were off limits to operational real-time observations a mere 10 years ago. Alternative power solutions for shore-based observing in remote areas and technologies that are not problematic during freeze-up conditions are also making much needed observations in areas difficult to maintain operational installations. In this paper, we discuss technologies AOOS has helped successfully implement to fill critical observing gaps, including remotely powered, high frequency (HF) radar that measure surface current, a low cost, real-time ice detection buoy system that stays in the water through freeze-up, two alternative water level technologies to traditional National Water Level Observing Network (NWLON) installations, and weather observing installations that share data using the Automated Information System (AIS), which is used primarily to track ocean vessels. These technologies not only respond to Alaska needs, but also have broader applications to other remote regions including international Arctic and Antarctic locations, and remote coasts of New England, the Pacific Northwest and the Pacific Islands.
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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) | 0.000 | 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 | 0.001 |
| 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 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".