MétaCan
Menu
← Back to cohort
Record W2984218724 · doi:10.22215/etd/2016-11385

Five Watermarks: Five Design Interventions on the Ottawa Civic Hospital that Explore the Potential for Water and Architecture to Assist in Inhabitant Well-Being

2016· dissertation· en· W2984218724 on OpenAlexaffabout
Jade Labonte-Gregory

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchitecturePsychological interventionArchitectural engineeringContemplationSpace (punctuation)Built environmentProcess (computing)Health careEngineeringPsychologyCivil engineeringNursingMedicinePolitical scienceComputer scienceVisual artsArt

Abstract

fetched live from OpenAlex

Five Watermarks explores the question of ‘healing’ in the built healthcare environment, and in particular asks what role the incorporation of water can play in increasing inhabitant well-being. The primary role of healthcare architecture is to house technological tools and medical professionals. This project searches for ways in which architecture can be further integrated into the healing process. Through the design of five “Watermarks” (water-centralized design interventions) on the Ottawa Civic Hospital, the influence of water on atmosphere and environment is explored. The interventions incorporate water as either a natural building material or a filtered substance for cleanliness, while investigating concepts of dream, wayfinding, hygiene, tranquility and contemplation, through space and matter. Five Watermarks studies water’s capacity to deconstruct the boundaries between the self and the environment and to unify the conditions of space, life and well-being.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes2
Has abstractyes

Explore more

Same topicUrban Green Space and Health→French-language works237,207→