Ideas and Perspectives: Max MACS – constraining the potential global scale of Marine Anoxic Carbon Storage for CO <sub>2</sub> removal
Bibliographic record
Abstract
Abstract. Marine Anoxic Carbon Storage (MACS) is a potential strategy for enhancing atmospheric CO2 removal (CDR) by sequestering organic carbon produced by terrestrial plants in stable, anoxic marine reservoirs. Initial results suggest that MACS could, in theory, operate at the gigatonne scale that would be required to impact global climate, with limited environmental risk and promising opportunities for co-benefits. However, several outstanding knowledge gaps make it challenging to quantify the actual potential global scale of MACS with confidence. To inform decisions about climate mitigation and trade-offs in the future, it is essential that we know how MACS implementation at scale would impact critical environmental and economic systems in the context of likely future scenarios. Building on the results of a workshop in Bucharest, Romania in 2025, we discuss the potential impacts of MACS activities on the ecology, biogeochemistry, economy, and community around the Black Sea, seafloor brines, and other anoxic marine sites. Quantifiable limits to the potential maximum feasible scale of MACS for CDR are organized into five criteria: (1) Durable storage site capacity; (2) Biomass sources and logistics; (3) Greenhouse gas balance; (4) Oxygen and sulfide impacts at the redoxcline; and (5) Impacts on dissolved organic matter or nutrients in the oxic zone. For each criterion, we evaluate the factors that could limit scale, our current state of knowledge, and the priority knowledge gaps that, if addressed, would improve our ability to estimate the potential global scale of MACS for CDR. Research is needed to understand its potential impacts at scale, but MACS is nonetheless worthy of serious consideration as a potential pathway for climate mitigation in coming decades.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".