Application and assessment of a membrane‐based pCO<sub>2</sub> sensor under field and laboratory conditions
Why this work is in the frame
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Bibliographic record
Abstract
The principle, application, and assessment of the membrane‐based ProOceanus CO 2 ‐Pro sensor for partial pressure of CO 2 (pCO 2 ) 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 pCO 2 measuring systems and the thermodynamic carbonate calculation of pCO 2 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 pCO 2 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 pCO 2 references (± 2 and ± 8 µatm for direct measurements and calculated pCO 2 , 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 pCO 2 calculation as compared to the direct measurement (up to 90 µatm).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it