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Record W2373019766

STUDY ON THE APPLICABILITY OF GREEN SPACE RATIO AS THE EVALUATION INDEX OF GREEN QUALITY FOR TRADITIONAL COURTYARD AREA

2013· article· en· W2373019766 on OpenAlexaboutno aff
Ren Jin-fen

Bibliographic record

VenueChengshi guihua · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)Index (typography)Urban green spaceQuality (philosophy)Quarter (Canadian coin)Architectural engineeringGreen buildingAgricultural engineeringEngineeringComputer scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Green space ratio has been widely used as the evaluation index of green quality for an area in city planning and construction. Although this evaluation index is suitable for most modern construction projects, it is very difficult for most projects located in traditional courtyard area to meet the established standard. This paper analyzes the reasons, compares a traditional courtyard area with a modern residential quarter on their carbon dioxide (CO2) sequestration values of green space, and concludes that green space ratio is not a good measurement for green space of courtyard area, and that the number of trees, integrated green coverage rate, green plot ratio, and CO2 sequestration may be more suitable to evaluate green quality in the courtyard area. Meanwhile, a method for calculating CO2 sequestration and making a more feasible evaluation index for green space is also presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.188
GPT teacher head0.320
Teacher spread0.132 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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