Improving Situational Awareness in the Arctic Ocean
Bibliographic record
Abstract
To successfully operate in a harsh environment like the Arctic Ocean, one must be able to understand and predict how that environment will evolve over different spatial and temporal scales. This is particularly challenging given the on-going and significant environmental changes that are occurring in the region. Access to the most recent environmental information provides timely knowledge that enables ship-based operations to proceed efficiently, effectively and safely in this difficult arena. Knowledge of the evolving environmental conditions during a field campaign is critical for effective planning, optimal execution of sampling strategies, and to provide a broader context to data collected at specific times and places. We describe the collaborations and processes that enabled an operational system to be developed to provide a remote field-team, located on USCGC Healy in the Beaufort Sea, with near real-time situational awareness information regarding the weather, sea ice conditions, and oceanographic processes. The developed system included the punctual throughput of near real-time products such as satellite imagery, meteorological forecasts, ice charts, model outputs, and up to date locations of key sea ice and ocean-based assets. Science and operational users, as well as onshore personnel, used this system for real-time practical considerations such as ship navigation, and to time scientific operations to ensure the appropriate sea ice and weather conditions prevailed. By presenting the outputs of the system within the context of case studies our results clearly demonstrate the benefits that improved situational awareness brings to ship-based operations in the Arctic Ocean, both today and in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".