Bibliographic record
Abstract
This research aims to compare waste collection methods and introduce a modern method of source separation, which has been implemented in 22 districts of Tehran. This project has utilised field methods, face-to-face training, questionnaires, and brainstorming sessions. The Nomand’s plan was implemented in 8, 13, and 14 regions, and the citizens received the plan well. In the following, Nomand’s plan was implemented in the 20 regions, and the results showed that in areas of the 20 regions, participation rates in the two areas were higher than in other places. At the beginning of the Nomand’s plan, the highest amount of waste collected in 20 regions was 391 kg, and during the plan, the highest amount of waste collected was recorded in 20 regions, equal to 1238 kg. Collecting dry waste directly from its source is highly effective. This method will yield successful results if the true value of dry waste is compensated to the individuals. Authorities must address this issue. The Nomad’s plan was first implemented in Tehran, allowing citizens to easily deliver their dry waste to Waste Organisation experts. This plan aims to decrease pollution and lower transportation costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".