Metrological challenges for measurements of key climatological observables. Part 3: seawater pH
Bibliographic record
Abstract
Abstract Water dissolves many substances with which it comes into contact, leading to a variety of aqueous solutions ranging from simple and dilute to complex and highly concentrated. Of the multiple chemical species present in these solutions, the hydrogen ion, H + , stands out in importance due to its relevance to a variety of chemical reactions and equilibria that take place in aquatic systems. This importance, and the fact that its presence can be assessed by reliable and inexpensive procedures, are the reasons why pH is perhaps the most measured chemical parameter. In this paper, while examining climatologically relevant ocean pH, we note fundamental problems in the definition of this key observable, and its lack of secure foundation on the International System of Units, the SI. The metrological history of seawater pH is reviewed, difficulties arising from its current definition and measurement practices are analysed, and options for future improvements are discussed in conjunction with the recent TEOS-10 seawater standard. It is concluded that the International Bureau of Weights and Measures (BIPM), in cooperation with the International Association for the Properties of Water and Steam (IAPWS), along with other international organisations and institutions, can make significant contributions by developing and recommending state-of-the-art solutions for these long standing metrological problems.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".