Contribution of non-carbonate anions to river alkalinity and overestimation of <i>p</i> CO <sub>2</sub>
Bibliographic record
Abstract
Abstract. Total alkalinity (TAlk) has long been used to evaluate the buffering capacity of aquatic systems. TAlk has also been used, together with measurements of either pH or dissolved inorganic carbon (DIC), to indirectly estimate the partial pressure of carbon dioxide (pCO2) in inland waters, estuaries, and marine systems. These estimates typically assume that carbonate and bicarbonate ions comprise nearly all the species contributing to TAlk; however, other inorganic and organic acids have the potential to contribute significant non-carbonate alkalinity. To evaluate the potential for error in using TAlk to estimate pCO2, we measured pH, TAlk, and DIC in samples of river water. Estimates of pCO2 derived from TAlk and pH measurements were markedly higher than pCO2 estimates derived from DIC and pH. We infer that this overestimate is due to the presence of significant non-carbonate alkalinity (NC-Alk). This study also describes the relative proportions of carbonate- and non-carbonate alkalinity measured in 15 river systems located in northern New England and the Canadian Maritimes. NC-Alk represents a significant buffering component in these river systems, and failure to account for NC-Alk (which cannot directly contribute to pCO2) leads to the overestimation of carbon dioxide release to the atmosphere.
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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) | 0.000 | 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".