CTD observation dataset from Chinese Arctic scientific expedition during 1999-2021
Bibliographic record
Abstract
From 1999 to 2021, China has conducted 12 Arctic scientific expeditions and obtained a large amount of valuable polar marine hydrological survey data. The CTD survey equipment over the years was MARKⅢ C CTD (for the 1st and 2nd Arctic scientific expeditions) and SBE911 Plus CTD (for the 3rd to 12th Arctic scientific expeditions). Data on conductivity, temperature, and depth of the marine vertical sections were obtained through fixed-point deployment. The survey areas included key sea areas 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 original 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, to form a dataset with a vertical resolution of 1m, including elements 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 evaluation, the quality of the processed data was good, and the difference between the dual temperature and salinity sensors after eliminating outliers was within a reasonable range. This dataset provides valuable in-situ data for the study of water mass distribution, circulation, marine environmental changes, and global climate change in the Arctic Ocean.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".