Davis Strait hydrographic mooring Level 2 data: temperature, salinity, and velocity measurements from the Davis Strait Observing System moorings, 2004 to 2022
Bibliographic record
Abstract
The Davis Strait observing system was established in 2004 to advance understanding of the role of Arctic – sub-Arctic interactions in the climate system by collecting sustained measurements of physical, chemical and biological variability at one of the primary gateways that connect the Arctic and subpolar oceans. Efforts began as a collaboration between researchers at the University of Washington’s Applied Physics Laboratory and the Canadian Department of Fisheries and Ocean’s Bedford Institute of Oceanography, but has grown to include researchers from the Greenland Institute of Natural Resources, Greenland Climate Institute, Danish Technological University, University of Alberta and University of Colorado, Boulder. The project is a component of the NSF Arctic Observing and Atlantic Meridional Overturning Networks, and the international Arctic-Subarctic Ocean Flux (ASOF) program, Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP), Global Ocean Acidification Observing Network (GOA-ON), Arctic Monitoring Assessment Programme (AMAP) and OceanSITES system. A mooring array spanning the entire Davis Strait has been in place nearly continuously since September 2004 as part of the Davis Strait observing system, collecting year-round measurements of temperature, salinity and velocity extending to the sea surface/ice-ocean interface. The mooring typically included 14 moorings, 4 on each shelf and 6 in the center of the strait, that are recovered and data offloaded each autumn. Exact mooring location, instrumentation, and deployment duration varied slightly over time. This dataset consists of Level 2 data from the Davis Strait mooring array. Each file contains data from a single sensor (e.g., MicroCAT temperature and salinity measurements or ADCP velocity measurements) at one mooring site collected during a single deployment (typically one year long). Files also include quality control flags. More details about the project can be found at https://iop.apl.washington.edu/project.php?id=davis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".