Designing pentapartitioned neutrosophic cubic set aggregation operator-based air pollution decision-making model
Bibliographic record
Abstract
Abstract Environmental pollution is a global concern that has economic and health implications. Therefore, proper estimation using precise modeling can help in decision-making to address this externality. In science and engineering, there are a lot of different theories to help deal with the complex frame of the environment. The prime objective of these theories is to impart a plan of action to handle fuzzy data more precisely. Furthermore, humans need a platform that can correctly assign a value to optimize credence in a belief system. The indeterminacy is further classified into contradiction, ignorance, and unknown by a pentapartitioned neutrosophic set. On the other hand, a cubic set characterizes both the combined and the crisp value. The study introduces pentapartitioned neutrosophic cubic set, as it illustrates all of these attributes, allowing credence to be appropriately handled. The study also explained its operational laws and aggregation operators. Finally, this technique is used to develop and evaluate the air pollution models in major Pakistani cities like Karachi, Lahore, Islamabad, and Peshawar. It will help the legislators to reevaluate current policies to mitigate this externality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".