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Record W4318460264 · doi:10.54097/hset.v28i.4157

The difference of the Biophilic Design between Canada and China

2022· article· en· W4318460264 on OpenAlexaboutno aff
Jinxiang Wang

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsChinaDilemmaSustainabilityNatural (archaeology)Urban designInstinctArchitectural engineeringSustainable developmentSociologyFocus (optics)Design elements and principlesEnvironmental ethicsComputer scienceUrban planningGeographyEngineeringPolitical scienceCivil engineeringEpistemologyEcologyLaw

Abstract

fetched live from OpenAlex

With the continuous increase of urban density, people gradually find that the city that is isolated from nature is contrary to the instinct of human desire for nature. The pro-nature design born to alleviate this dilemma has naturally become the focus of attention and the direction of future urban development. trend. The purpose of this paper is to find out the differences of the same concept in different environments and the reasons for the differences by studying and comparing the biophilic design cases and environmental differences between Canada and China. By listing and comparing the different human and natural environments of two countries, as well as the comparison of two cases, the article studies the different influences of people's living environment on the new biological design. Finally, it is concluded that the different levels of people's pressure and desire for nature will have an impact on biophilic design, and the degree of pressure and desire for nature is proportional to the number of biophilic elements in the design. This paper expects that biophilic design can focus on the sustainability and practicality of buildings, and at the same time, this paper provides information for future biophilic development and a theoretical basis for design.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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