Solid Waste Characterization and Recycling Potential for a University Campus in China
Bibliographic record
Abstract
Waste characterization is the first step to a successful waste management system. This paper explores the trend of solid waste generated on a university campus (Longzi Lake Campus of Henan Agricultural University) in China and the factors that influence the potential for recycling of the waste. Face-to-face interviews were carried out for 12 consecutive months on a campus in central China, and 416 interviewees were questioned. It was found that 7.32 tonnes of solid waste were generated on the campus each day, of which 79.31% were recyclable. The characterization of major waste streams are discussed, and the results are compared with comparable data from five universities in a range of other countries (Mexico, Canada, Malaysia, Nigeria, and Ethiopia). The annual growth of GDP per capita in China over the past five years before the research appeared to play an important role in the increasing of food waste on university campus, and the proportion of food waste is found to have a positive influence on recycling potential.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".