Urban Green Space as a Solution for Degenerative Diseases: A Perspective Based on a Literature Review
Bibliographic record
Abstract
The research aimed to analyse factors contributing to degenerative diseases, including exposure to chemicals and air pollution.This research is a literature review focusing on various environmental factors that contribute to degenerative diseases and presents a solution perspective involving the creation of green spaces as a means to mitigate the impact of these factors.The literature was collected by searching metadata on the following page: https://www.scopus.com/search/form.uri?display=basic#basic, using the keywords "degenerative diseases" and "pollutant".The data selection using the PRISMA method to identify the most relevant literature for reference in this research study.Each type of pollution or environmental factor contributing to degenerative diseases has been systematically analysed using NVivo 12 Pro.In total, 35 research titles explore different factors responsible for degenerative diseases, with a particular focus on environmental factors.The results of the analysis conducted by researchers based on these 35 articles were mapped using NVivo 12 Pro, categorizing them according to the types of factors responsible for degenerative diseases.It is noteworthy that the majority of these studies focused on the neurodegenerative type.Furthermore, from the pool of 35 selected articles, air pollution emerged as the most dominant cause, constituting 57% (20 articles) of the total literature.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".