Addressing the Impact of Environmental Xenobiotics in Coal-Fired Flue Gas
Bibliographic record
Abstract
Dangerous and unstable situations can result from the presence of environmental xenobiotics since their harmful effects on humans and ecosystems are often unpredictable, and building awareness of the environmental risk should be a main concern of humankind. The environmental xenobiotics in the flue gas from a fossil fuel-fired electrical generating station, such as particulate matter (PM), sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon dioxide (CO2), are analyzed in this study, since these xenobiotics are persistent pollutants. Mathematical models of the environmental pollutant vector, estimating the emission factors specific to fossil fuel combustion, are applied to the operation of thermal units in the Turceni electrical generating station, each of which produces a net electrical power of 330 MW. For each stack gas component in the pollutant vector, emission factors and pollutant concentrations are determined. A pattern is also examined depicting the mathematically modelled processes of resonant absorption of an environmental xenobiotic harmonic oscillation by an organism modulated as an absorbing oscillator structure. The xenobiotic concentration degree is represented through a spatial concentration vector, which allows further modelling and simulation of the oscillating regime of environmental xenobiotic absorption.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".