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Record W3155561803 · doi:10.21203/rs.3.rs-63053/v1

Investigating Visualization Principles for Heritage Rehabilitation

2020· preprint· en· W3155561803 on OpenAlexaffabout
Abobakr Al-Sakkaf, Samer El-Zahab, Saleh Ben Lasod

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsConcordia University
FundersKing Saud University
KeywordsVisualizationRehabilitationComputer scienceData sciencePsychologyArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Abstract Many countries of the world suffered the problem of historical neighborhoods within their urban entity, through various treatments and phases. Regardless of numerous treatments, the city in general is described as an organic entity that has past, present, and future. This past with its birth, originality, and transformation had to have relied on material and spiritual assets that surrounded it, and the urban texture that lived and continued in the peripheries of the cities during the past era is only cultural humanitarian production that interacted and was environmentally consistent and covered the human needs that were related to time and place. In this article, three international experiences were chosen from countries that are different in their geographic location and cultural heritage, and have analyzed their experiences to conserve the historical identity of cities, as follows: The first experience which is handled by the research is the experience of France in the renovation, conservation, and improvements of heritage sites. Any type of dealing on whatever level with historical areas (whether subject to maintenance, improvement or renewal) were subject and belonged to a general plan that was set based on a comprehensive view of the city as a whole, taking into account all elements that affect the problem, including economic, social, cultural and political matters. It is known that France holds advanced philosophical and theoretical heritage in the field of urban planning based on situations of the senior architects and theorists of planning in the past century. The second experience studied in this article takes place in the Kingdom of Saudi Arabia and presents astounding results in the field. It was renewed and was conserved by autonomous efforts by the rehabilitation of many historical cities and restoration of their vitality and spirituality to become attractive to tourism, heritage value, and investment. North America included the United State of America and Canada.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.006
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.484
GPT teacher head0.447
Teacher spread0.038 · 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 designTheoretical or conceptual
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
Published2020
Admission routes2
Has abstractyes

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