Research on multi-scale assessment and design of urban micro-landscape greening based on distributed computing frameworks
Bibliographic record
Abstract
Micro-landscape is a kind of green landscape designed to enhance the local landscape environment of the city along with the renewal of urban green space and the transformation of old city.The article adopts Hadoop technology and utilizes the Hadoop distributed computing framework to preprocess the data, constructs the urban micro-landscape greening evaluation system, and carries out research on four evaluation levels, namely, building façade landscape, multimedia landscape, water landscape, and landscape facilities.At the same time, based on the principal component analysis and factor analysis method for comprehensive evaluation, it is determined that the multimedia interaction factor is the most important factor affecting the effectiveness of micro-landscape greening.Then use SWMM model to design a city urban area, through SWMM model simulation to get the actual average annual runoff control rate of the demonstration area in 2023 is 59%, and the overall long-term goal of urban micro-landscape greening planning in 2020-2030 there is a gap, based on which put forward the urban micro-landscape greening design program.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".