Partisipasi masyarakat dalam pengelolaan sampah pemukiman di Kecamatan Senapelan Kota Pekanbaru
Bibliographic record
Abstract
The problem of waste management which is still a city problem, if the waste is not done properly it will cause problems. Uncontrolled piles of garbage due to human activities will have an impact on environmental problems such as decreasing the beauty of the city, the emergence of odors from waste decomposition, the occurrence of air pollution due to burning waste that interferes with public health and a source of disease for human health. This research was conducted in Senapelan District, Pekanbaru City from February to April 2022. The type of research is quantitative analytic with cross sectional. The population is all heads of families with a sample of 94 people with purposive sampling. The results showed that there was no influence of age, education, occupation, on community participation in residential waste management in Senapelan District, Pekanbaru City and there was an influence on infrastructure, knowledge, perception, on community participation in residential waste management in Senapelan District, Pekanbaru City. It takes a conducive environment and good infrastructure in waste management in settlements in Senapelan District, Pekanbaru City
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".