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Record W4235452301 · doi:10.32920/ryerson.14665029.v1

Resilietn Coupling: The Systems Of Coastally Dependent Communities

2021· preprint· en· W4235452301 on OpenAlexaboutno aff
Brandon Saverio Knight Bortoluzzi, Arthur Wrigglesworth

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Resilience (materials science)MultitudePsychological resilienceArchitectureFace (sociological concept)GeographyEnvironmental resource managementPolitical scienceEnvironmental planningEnvironmental ethicsSociologyComputer scienceEnvironmental scienceLawArchaeologySocial science

Abstract

fetched live from OpenAlex

As climate change becomes a more prevalent reality, rising sea levels are increasingly a threat to cities and communities in coastal regions. In light of this it is important to consider architecture’s role in the strategizing of defences and resilience. The major issue with traditionally implemented coastal defence programs, such as those considered by the US Army Corp of Engineers, is their brute force approach is repressively one dimensional, undermining the diverse, and complex realities of any community. Orienting itself in the diverse and complex communities of Atlantic Canada, this thesis operates in the face of these challenges and shortfalls. Instead a coupling of systems, activities and events in these coastal communities can make possible an architecture that accommodates, and makes visible, the realities of its changing environs at a multitude of scales, allowing the continued success of human settlement.

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)0.003
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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