Fuzzy UML and Petri nets modeling investigations on the pollution impact on the air quality in the vicinity of the Black Sea Constanta Romanian resort
Bibliographic record
Abstract
The purpose of this research is to investigate the use of an intelligent neural-fuzzy modeling strategy based on Unified Modeling Language (UML) diagrams and Petri nets models of the pollution sources impact on the air quality along the Romanian coast of Black Sea, especially in Constanta vicinity. This is possible by monitoring the physical and chemical parameters of the air quality, such as temperature, wind speed, Carbon Dioxide (CO2), methane (CH4), Nitrogen Oxide, ozone, water vapours concentrations provided by several “in-situ” measurements stations spread in the critical points from Constanta area. Moreover, we will try to disseminate the information collected and to investigate adequate actions to prevent the continuous degradation of the environment. The values of air quality-monitored parameters vary with the position of the sampling sites in quasi-large range; consequently a direct correlation between these indicators will be useful. Air pollution sources cause the “greenhouse effect“with a high impact on the live and fauna, degrading progressively the Black Sea ecosystems. Closing, in our research we try to present the benefit of the UML diagrams in combination with Petri nets models developed on a wide database concerning the air pollution degree inside Constanta Romanian Black sea resort to predict the future results.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".