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Record W4400624373 · doi:10.23977/acss.2024.080413

Protection model based on value assessment and vulnerability curve

2024· article· en· W4400624373 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2024
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Decision-Making Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Vulnerability (computing)StatisticsMathematicsComputer scienceComputer security

Abstract

fetched live from OpenAlex

Extreme weather has become one of the most serious challenges to humans' lives, its increasingly frequent occurrences affect the preservation and protection of many cultural buildings, to judge the cultural and historical value of a cultural building and its vulnerability to extreme weather, so as to further realize its preservation and protection. This paper establishes the historical and cultural value evaluation model and the vulnerability curve of cultural heritage buildings based on the AHP-Field method, Mann-Kendall test and quintile regression respectively. Taking Fujian Tulou as an example, we obtained the historical and cultural evaluation system and the vulnerability curve affected by heavy rain, and comprehensively considered the relationship between the two, and obtained comprehensive measures to protect Fujian Tulou buildings. Make feasible suggestions on how to protect ancient buildings in the case of heavy rain and provide reference for the evaluation and protection of other regions.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.679

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 communication0.0000.001
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 head0.035
GPT teacher head0.356
Teacher spread0.321 · 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