Reconsidering the role of carbonate ion concentration in calcification by marine organisms
Bibliographic record
Abstract
Abstract. Marine organisms precipitate 0.5–2.0 Gt of carbon as calcium carbonate (CaCO3) every year with a profound impact on global biogeochemical element cycles. Biotic calcification relies on calcium ions (Ca2+) and usually on bicarbonate ions (HCO3−) as CaCO3 substrates and can be inhibited by high proton (H+) concentrations. The seawater concentration of carbonate ions (CO32−) and the CO32−-dependent CaCO3 saturation state (ΩCaCO3) seem to be irrelevant in this production process. Nevertheless, calcification rates and the success of calcifying organisms in the oceans often correlate surprisingly well with these two carbonate system parameters. This study addresses this dilemma through the rearrangement of carbonate system equations which revealed an important proportionality between [CO32−] or ΩCaCO3and the ratio of [HCO3−] to [H+]. Due to this proportionality, calcification rates will always correlate as well with [HCO3−] / [H+] as they do with [CO32−] or ΩCaCO3 when temperature, salinity, and pressure are constant. Hence, [CO32−] and ΩCaCO3 may simply be very good proxies for the control by [HCO3−] / [H+], where [HCO3−] serves as the inorganic carbon substrate and [H+] functions as a calcification inhibitor. If the "substrate–inhibitor ratio" (i.e., [HCO3−] / [H+]) rather than [CO32−] or ΩCaCO3 controls biotic CaCO3 formation, then some of the most common paradigms in ocean acidification research need to be reviewed. For example, the absence of a latitudinal gradient in [HCO3−] / [H+] in contrast to [CO32−] and ΩCaCO3 could modify the common assumption that high latitudes are affected most severely by ocean acidification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".