Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> One important aspect of understanding ocean acidification is the nature and drivers of pH variability in surface waters on smaller spatial (i.e., areas up to 100 km<sup>2</sup>) and temporal (i.e., days) scales. However, there has been a lack of high-quality pH data at sufficiently high resolution. Here, we describe a simple optical system for continuous high-resolution surface seawater pH measurements. The system includes a PyroScience pH optode placed in a flow-through cell directly connected to the underway supply of a ship through which near-surface seawater is constantly pumped. Seawater pH is measured at a rate of 2 to 4 measurements min<sup>-1</sup> and is cross-calibrated using discrete carbonate system observations (total alkalinity, dissolved inorganic carbon and nutrients). This setup was used during two research cruises in different oceanographic conditions: the North Atlantic Ocean (December–January 2020) and the South Pacific Ocean (February–April 2022). Our findings reveal fine-scale fluctuations in surface seawater pH across the North Atlantic and South Pacific Oceans. While temperature is a significant abiotic factor driving these variations, it does not account for all observed changes. Instead, our results highlight the interplay between temperature, biological activity, and water masses on pH. Notably, the variability differed between the two regions, suggesting differences in the dominant factors influencing pH. In the South Pacific, biological processes appeared to be mostly responsible for pH variability, while in the North Atlantic, additional abiotic and biotic factors complicated the correlation between expected and observed pH changes. Although surface seawater pH exhibited fine-scale variations, it remained relatively stable over a 24-hour cycle due to reequilibration with atmospheric CO<sub>2</sub>. Thus, for the regions and time periods studied, ocean basin-scale analyses based on discrete samples collected during traditional research cruise transects would still capture the necessary variability for global CO<sub>2</sub> cycle assessments and their implications.
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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 | 0.000 | 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) | 0.154 | 0.026 |
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; both teacher heads agree on what is shown here.
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".