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Record W7075742063

Seismic vulnerability of churches: the effect of context-related characteristics

2019· article· en· W7075742063 on OpenAlexaboutno aff

Bibliographic record

VenueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)DocumentationSet (abstract data type)Vulnerability assessmentObject (grammar)Seismic risk
DOInot available

Abstract

fetched live from OpenAlex

In Italy, a systematic treatment of the vulnerability of churches and of the classification of their peculiar damage modes has been the object of extended studies. Starting from the definition of macro-elements that are the main components of a church structure, affected by specific and recurrent damage patterns, a series of the most frequent limit mechanisms has been defined and is currently adopted in practice in assessment procedures. This work investigates the possibility to apply these concepts and procedures to other regions with different church typologies and constructional traditions and to identify procedural modifications that may be necessary. The documentation of church damage, occurred in the region of Québec, and the seismic risk of the city of Montreal have motivated to set up a collaborative research program aimed at defining an approach suitable for assessing the vulnerability of historic churches in such territory. From a previously developed inventory, churches have been grouped according to their typology, pointing out the constructional characteristics of each group and the damage patterns that may or may not be applicable from the Italian mechanisms. A first prevision is made of other possible context-related damage modes. Structural analyses to confirm these assumptions are in progress.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.205
Teacher spread0.183 · 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 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
Published2019
Admission routes1
Has abstractyes

Explore more

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