Assessment of long-term stability of inductive conductivity sensors at Argo floats
Bibliographic record
Abstract
This study analyses accuracy and stability of salinity measurements collected by four Argo autonomous drifters with RBR Ltd. inductive conductivity sensors operating in the Pacific Ocean during the recent 2-4 years. Inductive sensors have advantages over traditionally used electrode-type cells due to their better resistance to surface contamination and low power requirements, resulting in more robust and accurate measurements and extended float lifetimes. Proper assessment of the quality of the data collected by autonomous drifters is challenging due to lack of reference information. An important part of Argo program is the Delayed-Mode Quality Control process including salinity drift analysis and correction using the ‘Owens-Wong Calibration’ (OWC) method based on objective mapping of available reference data. This method, however, can misinterpret imperfect reference data as sensor drift. In this study, analyzing OWC output we introduce a combination of visualization methods focused on the locations where reference data can be treated as problematic. These methods include the analysis of spatial locations of the ‘profile correction factor’ along the float trajectory, comparing reference salinity fields calculated by the OWC method to additional reference sources (climatologies) and comparative analysis of different floats operating in the same area using the same reference datasets. The results demonstrate high level of stability of inductive conductivity cells on Argo floats, making them promising alternative for traditionally used Argo float CTDs equipped with electrode-type conductivity sensors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".