Preliminary study on the classification of typical rural domestic waste in Jiangsu province
Bibliographic record
Abstract
Huashu Village in Nanjing, Qintan Village in Xuzhou and Jinchu Village in Taizhou were taken as the research areas. Field visits and questionnaires were used to investigate the local villagers’ willingness to separate waste, domestic waste disposal methods, technology acceptance and waste disposal modes. Based on SPSS, the relevance and difference of villagers’ classification performance were analyzed from three dimensions: villagers’ willingness to separate waste, participation and acceptance of technology. At the same time, the similarities and differences in the composition of domestic waste and the sorting of rural waste in different study areas were analyzed by means of statistical charts. The analysis results are as follows: (1)In terms of villagers’ willingness to sort and acceptance of technology, villagers have a certain willingness to sort, but there is a tendency for villagers to rely on the government. Regarding the acceptance of technology, villagers of Huashu Village and Qintan Village have slightly higher acceptance of technology than Jinchu Village.The proportion of people who actively separate the daily waste in Huashu Village is higher than that in the other two villages, and villagers of Huashu Village performs better in classification. (2) The age and cultural level of villagers are significantly related to their willingness to separate, participation and acceptance of technology, while their age is negatively correlated with their participation in waste classification and acceptance of technology. (3) The composition types of domestic waste in the study area are basically similar, mainly including organic waste and plastic waste. In terms of waste collection methods, the models of the three places are similar, mainly choosing the mode of individual dropping at the waste dropping point-transferring to the town for disposal, among which Huashu Village widely uses sorting collection bins to collect waste.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".