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Record W2971813979 · doi:10.2495/sdp-v14-n3-260-272

How to reinterpret an Alpine eco-monster. Application of the method of choice experiments for the design of a reuse project

2019· article· en· W2971813979 on OpenAlexvenueno aff
Marta Bottero, Antonio De Rossi, Andrea Ponzetto, Davide Viano

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Planning and Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsMonsterReuseArchitectural engineeringComputer scienceEngineeringArtArt historyWaste management

Abstract

fetched live from OpenAlex

This paper deals with the role of evaluation methods for supporting the design process of sustainable urban and territorial transformations. In particular, the article considers the technique of Choice Experiments (CE) and it proposes a real application of the method for driving the decision-making process related to the reuse project of an abandoned building located in the Italian Alps. The evaluation model is based on different attributes of the reuse project, both tangible and intangible, including internal organization, external areas, accommodation structures and cost. In the evaluation, a questionnaire has been developed for the investigation of the preferences of potential users with reference to alternative reuse scenarios. The results of the application allowed us to determine the importance of the different attributes for the definition of the reuse strategy as well as the economic value of the selected strategy. The study proposed in this paper represents an innovative context of application of the CE method, regarding the economic evaluation of architectural buildings and landscape. A second innovative element of the present research concerns the use of the Choice Experiments approach for supporting the design of alternative solutions for a complex decision making problem.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.332
Teacher spread0.291 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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