Exploration and Applications of Carbon Utilization and Sequestration Technologies
Bibliographic record
Abstract
In recent years, the surge in global energy demand due to rapid technological advances has led to a significant increase in greenhouse gas emissions, resulting in a climate and energy crisis. Against this backdrop, the challenge for the global energy sector is to weigh the advantages and disadvantages of different carbon capture, utilization, and storage (CCUS) technologies in order to select the most appropriate method for each situation. This study focuses on Enhanced oil recovery (EOR) and ocean sequestration technologies and analyzes their theories, advantages, obstacles, and applications. EOR technology improves the efficiency of crude oil extraction by storing carbon dioxide (CO2) in deep strata and reducing the viscosity of the oil. This technology not only helps to reduce greenhouse gas emissions but also increases the rate of extraction of crude oil, providing a double benefit that makes it a commercially viable technology. However, the challenge with enhanced oil recovery technology is the high cost of facilities, which may limit its development prospects. Ocean sequestration refers to the storage of CO2 in the deep seabed to delay its contact with the atmosphere. The delay in the time of CO2 emissions brings a huge time value. However, ocean storage also has negative impacts, such as excessive CO2 in the ocean leading to acidification of seawater, disrupting the stability of ocean pH values, and ultimately leading to the extinction of deep-sea organisms.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".