It's a Dirty Job But No One Has to Do It: Collecting Geophysical Data in the Arctic Ocean
Bibliographic record
Abstract
The history of the Arctic Ocean is largely unwritten, but can be glimpsed through the fragments of what we know. My career has been defined by finding more fragments to build a panorama of how this ocean basin was created and modified over the last 150 million years. In the Summer of 2021 I boarded the R/V Sikuliaq, UAF’s research vessel, to voyage to the central Arctic Ocean and explore the seafloor and sediments beneath it through the use of sound at various frequencies. Multichannel-seismic data were collected in August and September 2021 over the Northern Chukchi Borderland and Central Canada Basin from the R/V Sikuliaq. The data were acquired with two 520 cu inch GI airguns and a 200 meters (32 channels) streamer. These data were collected to image the stratigraphy on the Borderland and in the Basin to study the evolution of these features. The processed multichannel-seismic profiles from the Northern Chukchi Borderland show horsts with grabens continuous with those imaged from R/V Langseth in 2011. These basins are filled with syn-rift and post-rift stratigraphy. Stratigraphic sequences imaged on Northwind Ridge are segmented by multiple unconformities and minor structures. The origin of these unconformities may be related to the opening of Canada Basin and multiple generations of glacial ice contact over the bathymetric high. The seismic profile on Canada basin showed a prominent feature recognized as a basement, which seems to support the interpretation of the extinct mid-ocean ridge as an unsegmented, ultra-slow spreading ridge. To make this cruise happen, it was necessary to work around a variety of COVID-related obstacles. It was something of a miracle that we left the pier at all. Once we were in the North, we encountered heavy ice conditions that dictated continuous revision to our science plan. We managed to collect good data, which will define some of the ocean’s unknown history. In this lecture, I will present the basics of the history of the Arctic Ocean, how we were able to work there in summer of 2021 and some preliminary results. Presenter Bio Bernard Coakley was born in Detroit, Michigan and attended two of that state’s finer Universities, eventually earning a degree in Geology from the University of Michigan. He attended Louisiana State, where he earned an MS degree and continued on to Columbia University where he received an MPhil and PhD. He had a post-doc at the University of Wisconsin - Madison and returned to Lamont-Doherty as a soft money research scientist. During this time he began to work in the Arctic Ocean, which has become his almost exclusive obsession since 1993. After five years on soft money, he went to Tulane University as an Assistant Professor. For the last twenty-one years he has been at the University of Alaska Fairbanks. To better understand the Arctic Ocean, he has sailed on US Navy fast attack submarine (SCICEX 1993, 1995 and 1999), on icebreakers (USCGC Healy, R/V Sikuliaq and Polarstern) and on relatively unreinforced vessels (R/V Langseth). He has been to the North Pole six times, though he wonders why that geographic singularity excites anyone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".