Sea ice drift tracks from the Distributed Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition 2019 - 2021
Bibliographic record
Abstract
The largest ever network of autonomous ice-tethered buoys was deployed as a Distributed Network (DN) surrounding the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory (CO). This extensive network of 112 Global Positioning System (GPS) buoys and 12 multi-instrumented ice stations captured the annual cycle of Arctic sea ice drift and deformation for the first time as the DN traversed the Transpolar Drift Stream. GPS position data from buoys deployed during the year-long MOSAiC experiment capture sea ice drift and deformation at spatial scales ranging from 100s of meters to 200 kilometers (km) from late September 2019 into 2021. This dataset contains 216 quality-controlled drift tracks from buoys deployed at sites within a 45 km radius of the MOSAiC CO. Initial deployments began 26 September 2019 (Leg 1) with new deployments of buoys in mid-March-April 2020 (Leg 3), and August-September 2020 (leg 5). This dataset has been fully reprocessed to update drift tracks with the last of the data collected by buoys that were still operational after the initial download and processing. Complete temporal coverage of this data set is now 26 September 2019 through 23 May 2021.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.017 |
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".