Assessment of a pH optode for oceanographic moored and profiling applications
Bibliographic record
Abstract
Abstract As global ocean monitoring programs and marine carbon dioxide removal methods expand, so does the need for scalable biogeochemical sensors. Currently, pH sensors are widely used to measure the ocean carbonate system on a variety of autonomous platforms. This paper assesses a commercially available optical pH sensor (optode) distributed by PyroScience GmbH for oceanographic applications. Results from this study show that the small, solid‐state pH optode demonstrates a precision of 0.001 pH and relative accuracy of 0.01 pH using an improved calibration routine outlined in the manuscript. A consistent pressure coefficient of 0.029 pH/1000 dbar is observed across multiple pH optodes tested in this study. The response time is investigated for standard and fast‐response versions over a range of temperatures and flow rates. Field deployments include direct comparison to ISFET‐based pH sensor packages for both moored and profiling platforms where the pH optodes experience sensor‐specific drift rates up to 0.006 pH d−1. In its current state, the pH optode potentially offers a viable and scalable option for short‐term field deployments and laboratory mesocosm studies, but not for long term deployments with no possibility for recalibration like on profiling floats.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".