At‐sea intercomparison of three underway <i>p</i>CO<sub>2</sub> systems
Bibliographic record
Abstract
Abstract Ocean surface partial pressure of carbon dioxide ( p CO 2 ) is a key factor controlling air–sea CO 2 fluxes. Most surface p CO 2 data are collected with relatively large and complex air–water equilibrators coupled to stand‐alone infrared analyzers installed on Ships of OPportunity (SOOP‐CO2). This approach has proven itself through years of successful deployments, but expansion and sustainability of the future measurement network faces challenges in terms of certification, autonomy, and maintenance, which motivates development of new systems. Here, we compare performance of three underway p CO 2 measurement systems (General Oceanics, SubCtech, and Pro‐Oceanus), including a recently developed compact flow‐through, sensor‐based system. The systems were intercompared over a period of 34 days during two crossings of the subpolar North Atlantic Ocean. With a mean difference from the General Oceanics system of −5.7 ± 4.0 μ atm (Pro‐Oceanus) and −4.7 ± 2.9 μ atm (SubCtech) during the 1 st crossing, our results indicate potential for good agreement between the systems. The study highlighted the challenge of assuring accuracy over long periods of time, particularly seen in a worse agreement during the 2 nd crossing, and revealed a number of sources of systematic errors. These can influence accuracy of the measurements, agreement between systems and include slow response of membrane‐based systems to p CO 2 changes, “within‐ship” respiration due to biofouling, and bias in measurement of the temperature of equilibration. These error sources can be controlled or corrected for, however, if unidentified, their magnitude can be significant relative to accuracy criteria assigned to the highest‐quality data in global databases. The advantages of the compact flow‐through system are presented along with a discussion of future solutions for improving data quality.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".