Organic Coatings Reduce Dissolution Rate by an Order of Magnitude for Carbonate Minerals Produced by Marine Fish
Bibliographic record
Abstract
Abstract Marine carbonate production and dissolution are important components of the global carbon cycle and the marine alkalinity budget. Global carbonate production by marine fish (ichthyocarbonate) has been estimated to be as high as 9.03 Pg CaCO3 yr−1; however, the fate of ichthyocarbonate is poorly understood. High magnesium concentrations in ichthyocarbonate would traditionally suggest rapid dissolution under current marine conditions, but a correlation between dissolution rate and mol%MgCO3 has not been observed. Here, we aim to determine the role of organic coatings on dissolution rates of ichthyocarbonate in marine environments. We applied a combination of petrographic, geochemical, and microCT approaches to assess the quantity and distribution of organic matter in ichthyocarbonate produced by two species of marine fish, the Gulf toadfish (Opsanus beta) and the Olive flounder (Paralichthys olivaceus). We show that organic matter, including external coatings and embedded organic material, is volumetrically significant, ranging from 8.5% to 32.3% of ichthyocarbonate by volume. Bleach oxidation of external organic matter coatings increased the dissolution rate of ichthyocarbonate by more than an order of magnitude, suggesting these coatings serve to reduce reactive surface area of the mineral fraction in ichthyocarbonate. Assuming that organic coatings do not influence sinking rates, external coatings extend the depth of ichthyocarbonate persistence in the water column by ∼12–15×. Therefore, organic coatings are an important determinant of the role of ichthyocarbonate in the marine carbon cycle.
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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) | 0.001 | 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".