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Record W7003313520

At Sea Level

2020· article· en· W7003313520 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsMiamiNarrativeFlood mythContext (archaeology)Sea levelStormSiege
DOInot available

Abstract

fetched live from OpenAlex

At Sea Level delves into the impacts of rising sea levels and resultant architectural responses. Countries like Vietnam, the Netherlands, Bangladesh and cities like Hamburg, New Orleans, Miami and Vancouver have been seeking to manage rising water levels and protect its residents from its potential impacts. This rise coupled with stronger storms create the perfect formula to sea level rise destruction. The first portion of this graduate project explores current flood mitigation construction categorized into 5 typologies: Elevated, Dryproofed, Floodable, Amphibious and Floating. It also delves into the phenomenology of water as a an architectural medium for hygiene, for play, and for relaxation. Five precedents were selected to investigate these methodologies and phenomenologies.
\nThe second portion of the project is presented as a story of the Vancouver Aquatic Centre located in Vancouver, Canada. Through a collection of real and mythic historical evidence, a narrative loop
\ntraversing through both a family’s history and that of greater culture is set against the context of an aging piece of civic infrastructure. Under siege from rising sea levels, the building transforms in response to environmental and social conditions over a 180 year period to allow for water to be perceived as something beautiful; reinforcing architecture’s capability to re-frame something that is feared into something that is valued. Water was used to enhance experiences as opposed to abandoning them.
\nWater was the protagonist.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.191
Teacher spread0.170 · 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 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
Published2020
Admission routes1
Has abstractyes

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