Feasible engineering solutions to reduce carbon dioxide pollution in the atmosphere using natural and human made sinks
Bibliographic record
Abstract
Following the United Nations Framework Convention on Climate Change (UNFCCC) back in 1992, as well as the Kyoto Protocol in 1997, the United Nations made an ambitious plan, that from 2015 on wars, the main objectives of the world and all member states of the UN, are to pursue the climate safe economy and to promote the economic development for the third world countries, both of which highly rely on Sustainable Development Goals and the Paris Climate Agreement. The Industrial age technology that uses fossil fuels to produce energy is still the common practice that many, even the highly developed countries, use. The excessive usage and massive uncontrolled exploitation of natural resources, amongst which the most common are deforestation, coal, oil and natural gas extraction, can lead to increased and unbalanced mitigation of carbon dioxide content in the Earth's atmosphere, which can further lead to increased Earth's temperature. In this paper we will discuss the case study of Weyburn-Midale Carbon Dioxide project, viewed as the largest in the world human made carbon capture and storage project, located in Canada, in Midale, Saskatchewan. In addition, as a viable option, we will also present data on reforestation in the Amazon region in Latin America, as one of the most ambitious plans to restore the ecosystem, as well as a project of afforestation in the Shandong, China, which reflects both on environment and economic development of the region. All of these options should show that they are feasible enough to minimize carbon dioxide pollution of the atmosphere. Both options, human made sinks, as well as the natures path and its own way to reduce carbon pollution by increasing the amount of natural sinks, should be seen as a long-term plan that can benefit highly developed countries and also be sustainable and affordable to the countries with smaller economic power.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".