Carbon Mineralization: From Natural Analogues to Engineered Systems
Bibliographic record
Abstract
Carbon sequestration research and technology is motivated by concerns that increasing atmospheric C02 concentrations are causing changes to Earth's climate and ecosystems that have the potential to cause serious, negative impacts on human welfare (IPCC 2005, 2007). As a global society, we will need to greatly improve energy efficiency and conservation, and develop alternative and renewable energy sources, while implementing carbon sequestration strategies to stabilize the concentration of atmospheric C02. The carbon mineralization strategies reviewed in this chapter complement C02 storage in subsurface pore space. This promising approach for sequestering C02 is grounded in the fundamental processes that govern natural mineral dissolution and carbonate precipitation. Natural analogue sites allow for the study of the geochemical and biological transformation of C02 at the field-scale; drawing our attention to potential reaction pathways that can be exploited and utilized, but also to the limitations that must be overcome in geoengineered and industrial systems designed to accelerate carbonation. Further study of natural analogues may yield a better understanding of the reaction pathways required for efficient carbonation, the long-term stability of carbonate minerals at Earth's surface, and the monitoring required for long-term storage (...)
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 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".