MétaCan
Menu
Back to cohort
Record W4285005973 · doi:10.22215/etd/2022-15043

Saving Architectural Heritage: Climate Change Resilience and Conservation Management

2022· dissertation· en· W4285005973 on OpenAlexaff
Danielle Myronyk

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsCarleton UniversityCanadian HeritageMaple Leaf Foods
Fundersnot available
KeywordsClimate changeVulnerability (computing)Context (archaeology)Environmental resource managementPlan (archaeology)Architectural engineeringArchitecturePsychological resilienceEstateResilience (materials science)Vulnerability assessmentEnvironmental planningCultural heritageGeographyEngineeringComputer scienceEnvironmental scienceBusinessEcologyArchaeology

Abstract

fetched live from OpenAlex

This research paper will discuss the technical aspects of preserving historic to achieve resiliency to the ever-changing effects of climate change. As a case study, an updated conservation management plan will be created for Maplelawn, former estate and walled garden, that considers the architecture, the urban setting, and the landscape within the context of climate change and site constraints. This research will consist of a combination of digital storytelling and visual inspection as well as the use of a Climate Vulnerability Index (CVI) to determine levels of sensitivity to change, maintenance requirements, and risk assessments. Moreover, the ramifications of climate change will be explored in greater detail focusing on effects to the aesthetics, structure, and user comfort of the building. Lastly, the context of the site will be used to determine the building's and site's ability to evolve over time to improve its resiliency to climate change.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.272
Teacher spread0.232 · 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.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicConservation Techniques and StudiesFrench-language works237,207