Bibliographic record
Abstract
Urban areas are expanding due to more people living and moving into the bigger cities. \nAs the population grows in the whole world every year and predicts double within the \ncities till 2030, the urban exploitation will follow by approximately 60 per cent. When \nnew land is claimed the flora and fauna will have to be removed. In this essay we \ninvestigate how small urban areas can be optimized to incorporate green infrastructure \nwithin Stockholm. \n \nBy implementing green solutions in small spaces we hope to raise the awareness of \nthe importance of connecting people with green urban environments. This is to make \nthe cities more habitable, increase the biodiversity in educational purposes and to \ncoexist instead of disconnect with nature itself. We see pollution, species in decline and \nhuman stress as one of the big public health problems. By making a design proposal on \nÅsögatan 144 based on environmental psychological theories and studies about plant \nmaterial and green solutions we want to show how these solutions can be implemented \nto make a greener and more sustainable city that hopefully will lead to reduction of \npollution, species decline and human stress.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.516 | 0.391 |
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".