Bibliographic record
Abstract
Abstract. Since 2003, the state-of-the-art Canadian Coast Guard Ship (CCGS) research icebreaker Amundsen furrows the Canadian Arctic waters to support novel research endeavors and collect oceanographic data. This paper presents the data acquisition, the processing methods and an overview of the data collected during the 2021 expedition as the ship traveled over 30 000 km during 122 days across the Canadian Arctic Ocean, collecting sea surface, atmospheric and seabed underway measurements. A total of 266 casts of a conductivity, temperature and depth profiler mounted on a rosette (CTD-Rosette) were also conducted to monitor the main physical, chemical and biological parameters of the water column. More specifically, the data here presented were collected with the CTD-Rosette across historical sampling transects in Davis Strait, the NorthWater Polynya (NOW), and Cape Bathurst. A 182 km dedicated survey of Moving Vessel Profiler (MVP), equipped with CTD, transmissometer, dissolved oxygen, fluorescence, sound velocity sensors, was conducted across Hudson Strait. We also present an overview of the data collected by the underway systems (seabed, thermosalinograph and atmospheric). Such data are essential in understanding the impacts of climate warming on the unique environments of the Canadian Arctic Ocean. Amundsen Science supports and promotes easy access and sharing of such valuable data to the scientific community.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.030 | 0.026 |
| Insufficient payload (model declined to judge) | 0.177 | 0.163 |
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".