Expedition Arctic Ocean 2016 - Meteorological, Oceanographic and Ship Data Collected Onboard Icebreaker Oden during August to September 2016
Bibliographic record
Abstract
Arctic Ocean 2016 is a polar research expedition in collaboration with Canada with the two icebreakers Oden and Louis S. St-Laurent. The icebreaker Oden will depart from Longyearbyen, Svalbard, 8 August to meet up with the Canadian icebreaker Louis S. St-Laurent and launch the expedition. For six weeks, the vessels will operate in the Arctic Ocean, mainly in the Amundsen Basin and in areas around the underwater mountain ranges Lomonosov Ridge and Alpha Ridge. The aim of the Canadian research programme is to collect data in support of Canada’s extended continental shelf submission to the Commission on the Limits of the Continental Shelf, prepared in accordance with the United Nations Convention on the Law of the Sea (UNCLOS). The Swedish research is focused on environmental chemistry, marine geology and atmospheric research. On board Oden there will also be Danish and Norwegian researchers working with marine geology and ice management. This dataset contains meteorological, oceanographic and ship data collected during the Canadian-Swedish expedition Arctic Ocean 2016, which was an international research cruise using the icebreaker Oden. Data include: Meteorological variables: Air temperature, Humidity, Wind direction/speed, Atmospheric pressure, Cloud height/cloudiness, Photosynthetic Active Radiation (PAR). Oceanographic variables: Sea water temperature, Conductivity, Salinity and Sound velocity. Ship data: Position, Speed, Course, Water depth. Further metadata on the instrumentation and the individual variables can be found in the info file. Graphics and files describing the route can found in the package.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".