Natural Environmental Contaminants and the Impact of Green Technologies on Climate Change
Bibliographic record
Abstract
Environment has been defined as the natural world in which living things dwell and grow. There are certain factors which often pose challenges to the environment and are capable of interrupting capacity building as well as sustainability. This research evaluates such factors as environmental contaminants which include any chemical, biological, or radiological substance or matter that hurts air, water, soil or living organisms. However, with the advancement of green technologies on the environment, there is a revolutionary change with regards to clean energy production, solar power, reduction of emissions of carbon dioxide, use of alternative fuels and other technologies that are less harmful to the environment than fossil fuels. This research focuses on clean energy production such as solar energy and other technologies that serve the best purpose of reducing emissions of carbon dioxide usually generated by vehicles, motorcycles and fuel power generators majorly in the cities, particularly in the industrial environments. These emissions from vehicle engines, power generators, gas flaring and other environmental contaminants are very dangerous to human health. Many, who have ingested these emissions, have serious interference with their bodies’ internal functioning, causing diseases like cancer, itching in the eyes and respiratory disorders like asthma. The pressing need to explore green technologies for sustainability becomes imperative and this gave rise to this research. To achieve this aim, this research adopts the doctrinal research methodology in examining the natural environmental contaminants and the impact of green technologies on climate change. On this premise, this research recommends tremendous exploration of green technologies as a recipe for a sustainable environment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".