Evaluation of integrated waste management by using of Waste Reduction Model (WARM)- (Case study of Amol-Noor region, Iran)
Bibliographic record
Abstract
Lack of proper municipal solid management in the Northern provinces of Iran has led to damages to the natural resources, health, environment, social and economic conditions. Construction of solid waste processing plants such as compost and incineration, ignoring essential elements in waste management, cannot be as a successful strategy to solve the solid waste problems. Integrated management as a suitable and well-proved solution in many developed countries was proposed for Mazandaran province as a more reliable strategy to replace the existing conditions. In this paper, the results of study conducted for Amol and Noor region have been analyzed. Minimizing waste production, maximizing recycling and reuse, processing organic waste through digestion or composting process, producing refusing derived fuel (RDF) and using it in the waste incineration plants or Neka cement plant and finally disposing less than 10% of the waste in landfill is the proposed solution to improve the existing waste conditions. Implementing the proposed integrated management over a period of 20 years will lead to preventing from economic and environmental damages due to the loss of valuable natural resources and the release of various pollutants from landfills which is evaluated to be equivalent of about 28 billion dollars also, saving resources and energy estimated to be about 38 billion dollars.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".