A Study on the Landscape Elements and Preference of Rural Village - Focused on Daewon-Ri Sanoe-Myun Boeun-Gun, Chungbuk -
Bibliographic record
Abstract
This study aims to draw the basis documents for rural landscape management of Daewon-Ri through frequency analysis of landscape elements and preference analysis of rural landscape. The results are as follows. First, according to the frequency analysis of the landscape elements, a distant view is few effect characteristics in rural village landscape planning. It is acted as the landscape elements that degree of integration and skyline of the building to see more nearby than it are the most important. In addition, in the case of the establishment of the landscape management planning, the landscape elements in the close view is the most important. Second, It is thought that the scenery which natural environments and residential quarter match is the most desirable for the par of the landscape preference in the rural village. On the other hand, about the scenery of an old historic building, the residents of a city considers it as an affirmative factor of the rural village landscape, but rural village inhabitants are negative. Finally, it is thought that the excessive public designs by government sponsored enterprise are undesirable for the scene of the village.
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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".