High Arctic marine geophysical data acquisition
Bibliographic record
Abstract
Despite record-low sea ice extents over the past five years, the high Arctic Ocean remains one of the most difficult operational environments on Earth for marine geophysical data acquisition. Until 2006, the extent of seismic reflection data in the western Arctic Ocean (western, from a North American perspective) amounted to ∼3000 line-km. In 2008, the United States and Canada teamed up to embark on four years of joint marine operations to acquire in excess of 15,000 line-km of geophysical data reaching to the farthest points north. Each nation contributed an icebreaker to operate jointly to acquire seismic reflection, seismic refraction, shipborne gravity, single and multibeam bathymetry, and subbottom reflection data. This article presents some of the operational aspects of data acquisition in perennially ice-covered seas and demonstrates some of the outstanding data that resulted, focusing on the seismic components of the program. The multibeam-sonar component of the program is published by Armstrong et al. (2012).
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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 | 0.000 |
| 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) | 0.004 | 0.011 |
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; both teacher heads agree on what is shown here.
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".