A Quality Control Method for Ocean Data Based on Multi-time Scale Downsampling and Dynamic Threshold Strategy
Bibliographic record
Abstract
Ocean data quality control (QC) is important for ocean scientific research and resource management, which aims to identify data errors and ensure the reliability of ocean data. Modern ocean observation data are characterized by large volumes and complex association patterns. This results in low efficiency of manual QC and low accuracy of automatic QC. Therefore, the use of unsupervised deep learning models for ocean data QC has become a major trend in recent years. However, it faces some challenges: the powerful learning ability of deep learning models could learn too well from anomalous data; the determination of anomaly threshold has an important effect on the results of QC. To address these issues, we propose a QC method for ocean data based multi-time scale downsampling and dynamic threshold strategy (MTSDTS-QC). A Multi-time Scale Downsampling module is developed to extract the distribution of normal data at both large and small time scales. This process can reduce the interference of anomalous data in the subsequent process. Then, TimesBlocks are used to reconstruct the ocean data considering their multi-periodic characteristics. The difference between the original and the reconstructed data represents the degree of abnormality of the data. Furthermore, we propose a dynamic threshold strategy that can determine the optimal thresholds for different marine data based on the intrinsic characteristics of anomaly scores. Experimental results on marine meteorological data show that the MTSDTS-QC method outperforms 14 existing baseline methods. The generalizability of our model is also validated on a public industrial control dataset WADI.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".