Bibliographic record
Abstract
Abstract : While domestic icebreaking operations may fall among the Coast Guard's less glamorous assignments, this mission is important for maritime mobility and supports our national transportation infrastructure. Operations include establishing and maintaining tracks (paths through the ice) in connecting waterways during the winter navigation season, escorting vessels to ensure their transit is not impeded by ice, freeing vessels that become beset, clearing/relieving ice jams, removing obstructions or hazards to navigation, and advising mariners of current ice and waterways conditions. This vital icebreaking mission is executed domestically by one heavy icebreaker, nine ice-breaking tugs, 11 small harbor tugs, and 12 ice-capable buoy-tending vessels. In addition to U.S. Coast Guard assets, the Canadian Coast Guard operates two icebreakers on the Great Lakes. The USCG and Canadian Coast Guard keep each other advised on the location and status of icebreaking facilities/assets and coordinate operations to keep critical waterways open for commerce. A cooperative agreement between our two nations allows the assets from one country to conduct icebreaking operations in the territorial waters of the other, as necessary. Along the East Coast, icebreaking generally occurs to facilitate deliveries of home heating oil, critical supplies in isolated communities, and ferry services in its busiest ports. The Coast Guard's domestic icebreaking mission is at a critical juncture. As many icebreaking assets -- specifically the 140- and 65-foot icebreaking tugs -- are at or past their designed service life, the Coast Guard is initiating a project to extend the service life of the 140-foot icebreaking tugs. Another vital component of the continued success of the domestic icebreaking program is sustaining professional relationships with commercial industry stakeholders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.046 |
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".