Field Data for Sea Ice and Iceberg Drift Offshore Newfoundland and Labrador
Bibliographic record
Abstract
Abstract This paper is focused on new field data collected for drift behaviour of first-year sea ice, a multi-year ice floe and two icebergs offshore Newfoundland and Labrador. Offshore operations in ice environments require detailed knowledge of ice conditions. Moreover, reliable forecasts of ice drift behaviour for first-year sea ice and extreme ice features such as thick multi-year (MY) ice and icebergs are essential in supporting ice management activities and in supporting effective operational decision-making. Central to the development of improved drift forecasting models is the collection of new field data that can be used to improve understanding of the physical environment and to validate and improve predictive tools. Three ice drift beacons have recently been deployed offshore Newfoundland and Labrador. In the present paper, a description of the beacons used, deployment activities, as well as results from these beacons are reported, along with initial analysis of these data. These new data provide interesting and sometimes unexpected drift behaviour. Results from these beacons are analyzed in light of ocean current and wind data and conclusions regarding the correlations between these environmental conditions and observed drift behaviour are discussed for each case.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 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".