Helicopterborne EM ice thickness surveys during the SafeWin 2011 field campaign
Bibliographic record
Abstract
This report summarizes the results of airborne electromagnetic (EM) ice thickness surveys undertaken during the SafeWin winter field campaign in 2011, using a helicopter‐towed EM Bird. Surveys were flown during and after the RV Aranda sea ice cruise in the Sea and Bay of Bothnia, between March 2 and 7, 2011. Due to severe ice conditions, RV Aranda arrived late at her final destination in the Bay of Bothnia. Therefore, and due to further delays related to technical problems with the helicopter and contaminated fuel, surveys from the ship could only be performed on two days, before the ship had to return south. However, we decided to keep the EM Bird on land in Kokkola, and after some careful training by Alec Casey, Mikko Lensu was able to perform surveys on four more days. \nIn total, 11 flights were performed, covering large parts of the Bay of Bothnia, the Quarken, and the northern Sea of Bothnia. While some flights were designed to provide the best overview of the regional ice thickness distribution, several flights were performed over the buoy array in the region surrounding the ship, to observe thickness changes resulting from changes in ice deformation. \nSurveys were carried out by the University of Alberta (UofA), Edmonton, Canada, who was a sub-contractor of FMI. UofA participants were Alec Casey, John Lobach, and Christian Haas. \nThe EU SafeWin project aimed to improve the safety of winter navigation in the ice‐covered Baltic Sea and other polar seas. It includes modeling and observational studies. As part of the latter, two winter field campaigns have been performed to provide data for the development and improvement of models and ship‐in‐ice studies, and for their validation. Ice thickness is one of the key parameters governing navigation in ice. Therefore, extensive ground‐based, shipborne, underwater, and airborne ice thickness surveys have been performed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".