Bibliographic record
Abstract
SMOG is a form of horrible air pollution that has recently been declared as a public health emergency in Southeast Asia. This article will talk about the drawback of smog pollution and its outcomes on human health. Smog has become the most important issue for Pakistan, from some past years. Since 2011, nearly all areas of Pakistan especially Lahore has been repeatedly affected by smog. In Many previous types of research, the focus is on Smog and, its source, alarm systems, and safeguard, when a risky Environmental event like smog, the conclusion may be riskier than the event itself will cause if people take irrational actions due to lack of relevant awareness. So, examine people's attitudes and a reaction to smog is theoretically and realistically meaningful. Recent projects of coal as a source of energy, high rates of outpouring from unmonitored industries, a large number of automobiles on roads, play a major role in trends of deforestation to construct new roads and recently the burning of crops leftovers has added fuel to the fire. Vehicles increase by 9% compared to the last five years due to a lack of public transport systems. Pakistan, India, and Bangladesh emit the most hydrocarbons in their fuel emissions compared to SAARC (South Asian Association for Regional Cooperation) countries. As a result of these problems, Pakistan is facing its relatives, losses and various dangerous human diseases.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".