Physical Ice Management Operations - Field Trials and Numerical Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Physical ice management is a critical element of station keeping operations in waters characterized by drifting sea ice: it minimizes ice related downtime. Ice management represents also an important cost driver for Arctic offshore developments. The ice management fleet needs to be assembled considering the particular site, season of interest and corresponding expected environmental conditions, the particular facility capabilities to operate in ice covered waters, and the desired operability of the entire system. The paper describes the physical ice management trials performed during the Offshore Newfoundland Research Expedition in April 2015. These trials were planned and executed with the purpose to generate data to support the development of numerical models for simulating key aspects of the ice management operation. The results of this paper are applicable to ice management fleet design. Particular focus is on Arctic and sub-Arctic areas characterized predominantly by open water, but still containing the risk of sea ice invasions, and vessels expected to operate in such areas. Results from the field trials are presented and discussed. The paper elaborates on the schemes for numerical modeling of ice management, the challenges, and how field trial data can support the development of effective simulation tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it