A dataset of acoustic profiles from the 9<sup>th</sup> Chinese Arctic Expedition (2018)
Bibliographic record
Abstract
Ocean acoustic profile data are key to understanding the physical properties of the ocean, playing a vital role in improving the accuracy of underwater target detection and positioning, optimizing the design of underwater communication networks, and serving as an important indicator for monitoring changes in the marine environment. Direct observation of ocean acoustic profiles eliminates errors caused by model prediction or estimation, providing precise and reliable measurements. The data are of significant value for scientific research, technological development, environmental monitoring, resource exploration, and practical applications. During the 9th Chinese Arctic Scientific Expedition conducted in the summer of 2018, an acoustic Doppler current profiler was used to directly observe the speed of sound in the ocean for the first time. First-hand measured data of ocean acoustic profiles were obtained from the Chukchi Plateau, the Canada Basin, the Mendeleev Ridge, and other high-latitude regions of the Arctic Ocean. Based on the collection and organization of the acoustic profiler and synchronous CTD observations from the 9th Chinese Arctic Expedition, and with reference to expedition’s field records, the measured SSP data were processed through standardized quality control procedures, including unification, normalization, and outlier removal, using dedicated data-processing software. Ultimately, a dataset with a vertical resolution of 1 m is formed, including variables such as date, time, acoustic velocity, and water depth. It is one of the scientific outputs of the 9 th Chinese Arctic Scientific Expedition, and provides a data foundation for multidisciplinary polar research.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".