Calcium isotope fractionation by intracellular amorphous calcium carbonate (ACC) forming cyanobacteria
Bibliographic record
Abstract
Abstract The formation of intracellular amorphous calcium carbonate (ACC) by various cyanobacteria is a widespread biomineralization process, yet its mechanism and importance in past and modern environments remain to be fully comprehended. This study explores whether calcium (Ca) isotope fractionation, linked to ACC‐forming cyanobacteria, can serve as a reliable tracer for detecting these microorganisms in modern and ancient settings. Accordingly, we measured stable Ca isotope fractionation during Ca uptake by the intracellular ACC‐forming cyanobacterium Cyanothece sp. PCC 7425. Our results show that Cyanothece sp. PCC 7425 cells are enriched in lighter Ca isotopes relative to the solution. This finding is consistent with the kinetic isotope effects observed in the Ca isotope fractionation during biogenic carbonate formation by marine calcifying organisms. The Ca isotope composition of Cyanothece sp. PCC 7425 was accurately modeled using a Rayleigh fractionation model, resulting in a Ca isotope fractionation factor (Δ44Ca) equal to −0.72 ± 0.05‰. Numerical modeling suggests that Ca uptake by these cyanobacteria is primarily unidirectional, with minimal back reaction observed over the duration of the experiment. Finally, we compared our Δ44Ca values with those of other biotic and abiotic carbonates, revealing similarities with organisms that form biogenic calcite. These similarities raise questions about the effectiveness of using the Ca isotope fractionation factor as a univocal tracer of ACC‐forming cyanobacteria in the environment. We propose that the use of Δ44Ca in combination with other proposed tracers of ACC‐forming cyanobacteria such as Ba and Sr isotope fractionation factors and/or elevated Ba/Ca and Sr/Ca ratios may provide a more reliable approach.
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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 | 0.000 | 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 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".