The progressive routes for carbon capture and sequestration
Bibliographic record
Abstract
Abstract The global warming is directly related to the increased greenhouse gas emissions from both natural and anthropogenic origins. There has been a drastic rise in the concentration of CO2 and other greenhouse gases since the industrial revolution primarily due to the intensifying consumption of fossil fuels. With the need to reduce carbon emissions and mitigate global warming certain strategies relating to carbon capturing and sequestration are indispensable. This paper comprehensively describes several physicochemical, biological and geological routes for carbon capture and sequestration. The trend of the increase in greenhouse gases over the years is illustrated along with the global statistics for fossil fuels usage and biofuels production. The physicochemical carbon capturing technologies discussed include absorption, adsorption, membrane separation and cryogenic distillation. The algal and bacterial systems, dedicated energy crops and coalbed methanogenesis have been vividly explained as the biological routes for carbon sequestration. The geological carbon sequestering route centers on biochar application and oceanic carbon storage. A systematic survey has been made on the origin and impact of greenhouse gases along with the potential for sequestration based on some fast‐track and long‐term sequestration technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".