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Record W4388629358 · doi:10.3390/buildings13112839

Environmental Assessments in Architectural Competitions in Poland in the Years 2018–2022

2023· article· en· W4388629358 on OpenAlexaboutno aff
Magdalena Grzegorzewska, Andrzej Kaczmarek, Paweł Kirschke

Bibliographic record

VenueBuildings · 2023
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityCompetition (biology)Process (computing)Architectural engineeringOccupancyRenewable energyEnvironmental impact assessmentEnvironmental economicsRisk analysis (engineering)EngineeringEnvironmental resource managementComputer scienceBusinessEconomicsPolitical science

Abstract

fetched live from OpenAlex

This paper discusses environmental and energy-saving factors in architectural competition procedures. The final assessment of sustainability and environmental aspects of a building is a derivative of the decisions taken during the entire project execution process, especially those taken at the initial stage. Notifications concerning architectural competitions, both in Poland and other European countries, as well as in the United States and Canada, increasingly often list sustainability as a necessary criterion. Aspects such as the use of renewable energy sources, energy efficiency solutions or reducing a building’s occupancy costs most often appear as guidelines. In this paper, the authors discuss the essence of solutions in architectural competition procedures announced in Poland in the years 2018–2022, using 154 cases as an example and setting them against the background of Europe. The types and level of detail of the selected criteria and their frequency of occurrence were examined. The study unveiled the absence of comprehensive guidelines in the processes of nationwide architectural competitions that take sustainability into account. The lack of definitions for environmental and energy objectives has a significant impact on their integration into competition designs, and reduces the chances of adapting and adopting these aspects at a further stage. Based on the analysis, a range of recommendations were formulated for implementation in competition procedures.

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 score0.309
Threshold uncertainty score0.436

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.248
Teacher spread0.237 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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