Sustainable sequestration of carbon dioxide - A review
Bibliographic record
Abstract
Among various GHG gases causing global warming, the contribution by CO2 alone is about 60%. Post combustion carbon capture is most viable technique compared to pre-combustion and oxy-fuel combustion CO2 capture techniques used for conventional coal based thermal power plants. Cryogenic separation, chemical absorption, adsorption, membrane based separation etc. belong to post combustion carbon sequestration technology however these methods have some or other disadvantages. Ocean injection results in lowering of pH of sea water thus affecting bacteria zooplankton and benthos species. Moreover following a considerable period of time, the stored CO2 can leak. Controlled addition of CO2 in ready-mix concrete, as produced in the United States, Canada and Singapore improves the compressive strength without sacrificing performance or durability. Microalgae consumes substantial quantity of carbon dioxide (1Kg dry algae biomass consumes about 1.83 Kg CO2) and hence very effective in bio-fixation of CO2 waste as well as in improvement of air quality. Accumulation of oil (about 20 to 50% weight of dry biomass) and fast growth of microalgae make microalgae cultivation a commercially interesting and promising technology to mitigate global warming problem and generation of bio-fuel alongwith other benefits namely production of nutrient dense foods, chemicals and fertilizer.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".