Air Quality comparison between two major cities in Romania: Timisoara vs Iasi
Bibliographic record
Abstract
The quality of air is becoming a highly important factor in our days.Today an overwhelming percentage on industry is based in the metropolitan areas.This is a reason why metropolitan areas are becoming more and more polluted.With the pollution becoming higher and higher the quality on life in general decreases in major cities.Air quality slowly becomes a determining factor when choosing to move from city to city with the job.One example of how air pollution affects communitiesis Beijing, in the last 6 months the population decreased by approximately 0.5%, the percentage isn't so big, but need to take into account that the numberof citizens always was growing in Beijing.People in our days tend to look for cleaner areas with low pollution where to have a healthier life.In my pervious paper I've talked about 2 devices that can measure following pollutants: PM2.5, PM 10 and NOx in real time, this device will be very helpful in determining the most common pollution factors in real time.What I'm trying to accomplish in this study is a comparison between two major cities in Romania, whom are big metropolitan areas and biggest industrial centers in their region.The cities that I've chose to compare are Timisoara and Iasi.Both cities have approximately the same number of inhabitants and also both of them have an emerging industry.Across this paper I will compare the traffic, industrial waste released in air, number of air quality measurement stations and where they are placed, the amount of precipitation per year and of course following pollutants: PM 2.5, PM 10 and NOx.For now, I will compare official info's from both cities.But after my device will be ready, I will go personally and measure each pollutant agent personally.I will try to determinate which of these two cities has a friendlier environment to live.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".