The Distribution of Massive Ice Features by Ice Types from Multi-Year Upward Looking Sonar Ice Draft Measurements
Bibliographic record
Abstract
Abstract Modem ULS instruments provide year-long continuous measurements of sea ice drafts with an unprecedented time resolution of 1-2 seconds and a horizontal resolution of approximately 1 m. In this paper we analyze multi-year moored ULS measurements of sea ice in the Chukchi and Beaufort Seas and off Northeast Greenland. Recently, the massive ice feature (MIF) parameter has been developed to provide a robust, geometrical characterization of all potentially hazardous marine ice features within a ULS ice draft data set. An MIF episode is the set of consecutive ice draft values which exceed a specific minimum ice draft (e.g. 1.2 or 2.0 m) having total cross-sectional area above a specified value (e.g. 250 m2). A database of all MIF episodes is prepared including area, distance and ice drafts (mean and maximum). Previous research has shown the MIF database from year-long mooring measurements to be a complete and robust characterization of potentially hazardous features. In this paper, we apply algorithms that detect different types of sea ice features: large singular ice keels, hummocky ice, thick brash ice, old or multi-year ice and marine glacial ice (icebergs and ice islands). These ice types are associated with individual MIF episodes so that each episode can be associated with one or more ice type. There are large variations in the numbers of MIF episodes among regions, with NE Greenland having the most frequent occurrences of MIFs. For NE Greenland approximately 40% of MIF episodes can be identified as large keels and less than 15% as hummocky ice episodes; however, the hummocky ice episodes typically have considerably larger crosssectional areas than large keels. There are fewer occurrences of multi-year ice and very rare occurrences of marine glacial ice. The MIF analysis results provide improved quantitative values for pressure loading of sea ice on offshore platforms and ships, as well as understandings about the nature of sea ice deformation and age characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".