SP.7-5 : Low Impact Development Projects as Green Infrastructure in Korea
Bibliographic record
Abstract
LID is based on source control of stormwater, decentralized rainwater management, site-design connected with engineering techniques and land use planning, and natural drainage system. Also, LID is a kind of green infrastructure aiming to escape the limitation of gray infrastructure characterized by conventional, centralized and larger scale stormwater treatment system based end-of-pipe. The term LID has been generally used in U.S.A. and Canada. LID is directly related to water sensitive urban design of Australia and sponge city of China. In Korea, LID has been widespread since 2010. LID project of Asan new town by Korea Land & Housing Corporation is the first case at urban scale. Since then, LID projects such as Echo Delta, Songsan Green City, and 6-4 living area of Sejong City have been developed. Ministry of Land, Infrastructure and Transport and Environment have been working on institutionalization and planning of LID. Now, there are many examples of planning, designing and construction of LID in Korea. LID has become a stormwater management system as green infrastructure under consideration in all development activities of Korea. With the new green infrastructure, LID stands at the starting line in Korea. In the future, the LID should be reflected in district unit planning guide, urban basic plan and management plan to increase the status as green infrastructure linked with urban regeneration and smart city. And the legislation on LID is needed.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".