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Record W2587013937 · doi:10.21307/acee-2016-005

Campus Development And Downtown Regeneration – Perspectives For Katowice

2016· article· en· W2587013937 on OpenAlexaboutno aff
Filip Piaścik, Michał Stangel

Bibliographic record

VenueArchitecture Civil Engineering Environment · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Development and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)SituatedQuarter (Canadian coin)DowntownArchitectureAttractivenessSubject (documents)Library scienceSociologyPolitical scienceGeographyArchaeologyComputer science

Abstract

fetched live from OpenAlex

Abstract The scope of the paper is to present the issues concerning the revitalization of an area situated in the centre of Katowice, where an academic quarter of the University of Silesia is emerging. Although this problem has been subject of numerous research works, workshops and competitions, the city of Katowice, despite many investments, has not managed to fully implement the revitalization of this area. Research activities indicated the deficiency in the integration of student functions. The surface of the area is about 10 hectares, located at the quarter comprising Warszawska, Bankowa and Dudy-Gracza Streets and the Rawa river boulevards. In May 2014 the Municipality of Katowice and University of Silesia announced the workshops on concepts of integrating an urban student centre, with the participation of the team from the Faculty of Architecture, Silesian University of Technology. The workshops were focused on analysing the situation of the program and design elaboration for the area in question. The proposed solutions were targeted at creating a student centre integrated with other public sites of Katowice, improving the attractiveness of the Rawa riverside, and providing a competitive advantage for the city of Katowice over other university cities in Poland.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 designQualitative
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

Citations2
Published2016
Admission routes1
Has abstractyes

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