Contribution of non-carbonate anions to total alkalinity and overestimation of pCO2
Bibliographic record
Abstract
Abstract. Total alkalinity (TAlk) has long been used to eval-uate the buffering capacity of aquatic systems. TAlk has also been used, together with measurements of either pH or dis-solved inorganic carbon (DIC), to indirectly estimate the par-tial pressure of carbon dioxide (pCO2) in inland waters, estu-aries, 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 higher than pCO2 es-timates derived from DIC and pH by 13–66 %. 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 alkalin-ity measured in 15 river systems located in northern New England (USA) and New Brunswick (Canada). NC-Alk rep-resents a significant buffering component in these river sys-tems (21–∼100 % of TAlk), and failure to account for NC-Alk (which cannot directly contribute to pCO2) leads to the overestimation of carbon dioxide release to the atmosphere. 1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".