A dataset of CTD temperature and salinity observations from Chinese Arctic scientific expeditions (1999–2021)
Bibliographic record
Abstract
From 1999 to 2021, China carried out 12 Arctic scientific expeditions, during which a large amount of valuable polar marine hydrological survey data was collected. CTD survey equipment over the years included the MARKⅢ C CTD (for the 1st and 2nd expeditions) and the SBE911 Plus CTD (for the 3rd to 12th expeditions). Data on conductivity, temperature, and depth of the marine vertical sections were obtained through fixed-point deployments. The survey areas covered key regions such as the Central Arctic Passage, the Chukchi Sea, the Chukchi Sea Shelf, the Canadian Basin, the Bering Strait, the Bering Sea, the Nordic Seas, and the Pacific sector of the Arctic Ocean. The raw data were processed and quality-controlled through processes such as data format conversion, data editing, filtering and lag processing, conductivity correction, heave correction, calculation of derived parameters, generation of iso-depth spacing files, and output of ASCII files, resulting in a dataset with a vertical resolution of 1m. The dataset contains variables such as time, longitude, latitude, pressure, depth, temperature, salinity, density, sound speed, and potential temperature. The quality of the processed data was further evaluated by comparing the data differences between the two sensors of the same variable and drawing the T-S point aggregation diagram. After eliminating outliers, the differences between paired temperature and salinity sensors remained within reasonable limits, confirming the high quality of the processed dataset. This dataset provides valuable in-situ observations for the study of water mass distribution, circulation patterns, marine environmental changes, and global climate change in the Arctic Ocean.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".