Air Oxygen Calibration of Oxygen Optodes on a Profiling Float Array
Bibliographic record
Abstract
Abstract Aanderaa optode sensors for dissolved oxygen show remarkable stability when deployed on profiling floats, but these sensors suffer from poor calibration because of an apparent drift during storage (storage drift). It has been suggested that measurement of oxygen in air, during the period when a profiling float is on the surface, can be used to improve sensor calibration and to determine the magnitude of sensor drift while deployed in the ocean. The effect of air calibration on oxygen measurement quality with 47 profiling floats that were equipped with Aanderaa oxygen optode sensors is assessed. Recalibrated oxygen concentration measurements were compared to Winkler oxygen titrations that were made at the float deployment stations and to the World Ocean Atlas 2009 oxygen climatology. Recalibration of the sensor using air oxygen reduces the sensor error, defined as the difference from Winkler oxygen titrations in the mixed layer near the time of deployment, by about tenfold when compared to errors obtained with the factory calibration. The relative error of recalibrated sensors is <1% in surface waters. A total of 29 floats were deployed for time periods in excess of one year in ice-free waters. Linear changes in the percent of atmospheric oxygen reported by the sensor, relative to the oxygen partial pressure expected from the NCEP air pressure, range from −0.9% to +1.3% yr−1 with a mean of 0.2% ± 0.5% yr−1. Given that storage drift for optode sensors is only negative, it is concluded that there is no evidence for sensor drift after they are deployed and that other processes are responsible for the linear changes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".