Current calcite (CaCO3) dissolution at the seafloor caused by anthropogenic CO2 (NCEI Accession 0176672)
Bibliographic record
Abstract
This dataset consists of current CaCO3 dissolution at the seafloor caused by anthropogenic CO2 in the World Oceans. This dataset contains the main results from Sulpis et al. (PNAS, 2018). All the variables have a 1x1 degree resolution. It can be used to compute calcite dissolution at the seafloor for changing bottom-water chemistry, calcite rain rates or current speeds, for instance. Oceanic uptake of anthropogenic CO2 leads to decreased pH, carbonate ion concentration, and saturation state with respect to CaCO3 minerals, causing increased dissolution of these minerals at the deep seafloor. This additional dissolution will figure prominently in the neutralization of man-made CO2. Yet, there has been no concerted assessment of the current extent of anthropogenic CaCO3 dissolution at the deep seafloor. Here, recent databases of bottom-water chemistry, benthic currents, and CaCO3 content of deep-sea sediments are combined with a new rate model to derive the global distribution of benthic calcite dissolution rates and obtain primary confirmation of an anthropogenic component. By comparing pre-industrial with present-day rates, we determine that significant anthropogenic dissolution now occurs in the western North Atlantic, amounting to 40-100% of the total seafloor dissolution at its most intense locations. At these locations, the calcite compensation depth has risen ~300 m. Increased benthic dissolution was also revealed at various hot spots in the southern extent of the Atlantic, Indian and Pacific Oceans. Our findings place constraints on future predictions of ocean acidification, are consequential to the fate of benthic calcifiers, and indicate that a by-product of human activities is currently altering the geological record of the deep sea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".