Application and assessment of a membrane‐based pCO<sub>2</sub> sensor under field and laboratory conditions
Bibliographic record
Abstract
The principle, application, and assessment of the membrane‐based ProOceanus CO2‐Pro sensor for partial pressure of CO2 (pCO2) are presented. The performance of the sensor is evaluated extensively under field and laboratory conditions by comparing the sensor outputs with direct measurements from calibrated pCO2 measuring systems and the thermodynamic carbonate calculation of pCO2 from discrete samples. Under stable laboratory condition, the sensor agreed with a calibrated water‐air equilibrator system at −3.0 ± 4.4 µatm during a 2‐month intercomparison experiment. When applied in field deployments, the larger differences between measurements and the calculated pCO2 references (6.4 ± 12.3 µatm on a ship of opportunity and 8.7 ± 14.1 µatm on a mooring) are related not only to sensor error, but also to the uncertainties of the references and the comparison process, as well as changes in the working environments of the sensor. When corrected against references, the overall uncertainties of the sensor results are largely determined by those of the pCO2 references (± 2 and ± 8 µatm for direct measurements and calculated pCO2, respectively). Our study suggests accuracy of the sensor can be affected by temperature fluctuations of the detector optical cell and calibration error. These problems have been addressed in more recent models of the instrument through improving detector temperature control and through using more accurate standard gases. Another interesting result in our laboratory test is the unexpected change in alkalinity which results in significant underestimation in the pCO2 calculation as compared to the direct measurement (up to 90 µatm).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".