Oxygen optodes on oceanographic moorings: recommendations for deployment and in situ calibration
Bibliographic record
Abstract
Increasing interest in the deployment of optical oxygen sensors, or optodes, on oceanographic moorings reflects the value of dissolved oxygen (DO) measurements in studies of physical and biogeochemical processes. Optodes are well-suited for moored applications but require careful, multi-step calibrations in the field to ensure data accuracy. Without a standardized set of protocols, this can be an obstacle for science teams lacking expertise in optode data processing and calibration. Here, we provide a set of recommendations for the deployment and in situ calibration of data from moored optodes, developed from our experience working with a set of 60 optodes deployed as part of the Gases in the Overturning and Horizontal circulation of the Subpolar North Atlantic Program (GOHSNAP). In particular, we detail the correction of drift in moored optodes, which occurs in two forms: (i) an irreversible, time-dependent drift that occurs during both optode storage and deployment and (ii) a reversible and pressure-and-time-dependent drift that is detectable in some optodes deployed at depths greater than 1,000 m. The latter is virtually unidentified in the literature yet appears to cause a low-bias in measured DO on the order of 1 to 3 µmol kg−1 per 1,000 m of depth, appearing as an exponential decay over the first days to months of deployment. Comparisons of our calibrated DO time series against serendipitous mid-deployment conductivity-temperature-depth (CTD)-DO profiles, as well as biogeochemical (BGC)-ARGO float profiles, suggest the protocols described here yield an accuracy in optode-DO of ∼1%, or approximately 2.5 to 3 µmol kg−1. We intend this paper to serve as both documentation of the current best practices in the deployment of moored optodes as well as a guide for science teams seeking to collect high-quality moored oxygen data, regardless of expertise.
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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.019 |
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".