Bibliographic record
Abstract
Summary Carbon dioxide being the most anthropogenic greenhouse gas,it needs to be isolated from entering into atmosphere. Carbon capture and storage is process that captures CO2 emitted from various sources, separates it from other gases and stores it in a safe place preferably in underground geological formations for large period of time. It is then purified and monitored so that can be made to reuse. Monoethanolamine, zeolitic imidazolate framework, microalgae, membranes etc are utilized to capture CO2. Post-combustion, pre-combustion and oxyfuel combustion along with chemical looping combustion are technologies for scrubbing CO2. The properties of CO2 being easily miscible and readily dissolving in oil with impurities makes it capable for numerous applications such as in producing oil by enhanced oil recovery (EOR), Bio CCS Algal Synthesis etc. CO2-EOR operatiion is capable to produce million barrels of oil and extend the field’s lifetime as in case of Weyburn Oil Field in Canada. The physical storage of CO2 is technically the most feasible direction provided that the associated safety and sustainability issues can be met and new materials for CCUS process at low cost are urgently found so that so that fossil based systems with carbon capture are cost competitive.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".