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Record W7026854888

The bioclimatic urban planning, a case of study: The railway workshops area in Bozen, Italy

2011· article· en· W7026854888 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System University of Ferrara (University of Ferrara) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsZoningSustainabilityWork (physics)Urban planningClimate changeLand-use planningQuarter (Canadian coin)Urban climateUrban area
DOInot available

Abstract

fetched live from OpenAlex

This work develops an analysis and design method to improve the sustainability in urban planning of modern cities.\nAs in most of the important cities across Europe, also in Bozen, city administration decided to transform the old railway workshops area into a new modern quarter near the old city centre and, to pursue this objective, it was announced an international design competition.\nOperating in these kind of areas can radically change all the climate environment of the city, considering the low buildings density and the nearness to the old city centre, as demonstrated by recent studies.\nWithin this context, this article presents the planning methodology developed to reduce the impact on the climate environment of the city by improving bioclimatic solutions at urban scale.\nStarting from a climate datasets analysis, it was operated a complete solar and ventilation simulation of the entire city to evaluate opportunities and treads linked to the characteristics of project site.\nThe outputs of the simulations provided a complete dataset about solar and ventilation behavior of the area, considering sun and wind the most influent variables for the micro-climatic environment of this part of the city.\nIn conclusion, crossing these data by each other, it was possible to elaborate a zoning of the project site to identify the better strategies to improve the sustainability in urban planning of the area.

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)0.002
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 score0.050
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.116
GPT teacher head0.270
Teacher spread0.155 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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