Measuring the Cost of Noise Pollution and Its Impact in Reducing Corporate Profits, Income Tax Collections, the Treasury, the National Economy and the Welfare of Society
Bibliographic record
Abstract
The aim of this study to define the concept of noise pollution and the cost of pollution from noise, it also aims to explain the cost of noise pollution on the profits of industrial enterprises, in addition to the recognition of the role of the Jordan Phosphate Mines Company to reducing the impact of noise pollution on workers, as well as the role played by the reduction of the costs of pollution and its impact on the profits of noise pollution to reducing treasury revenues. And therefore could not measure the impact of the company incurring the costs caused by noise pollution on company profits in the absence of a clear classification of the causes of the cost and thus the difficulty of measuring the impact on the profits of the company, the results of the study also indicated that there are costs of noise pollution and a clear impact on the profits of the company. This study focuses on the economic and social aspects which are of crucial importance in noise pollution that strongly affect the performance of the workers, the profits of industrial enterprises, and concludes that the noise pollution impact on staff performance and production as well as tax collections, the treasury and the national economy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".