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Record W4229762886 · doi:10.22215/etd/2012-07200

How can landscape design improve the social and psychological conditions in the city improving the ecological stewardship of water?

2012· dissertation· en· W4229762886 on OpenAlexaboutno aff
Maria Carrier

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Planning and Landscape Design
Canadian institutionsnot available
Fundersnot available
KeywordsDirtResource (disambiguation)Stewardship (theology)CreaturesArchitectural engineeringEnvironmental stewardshipEnvironmental planningPublic spaceSpace (punctuation)Civil engineeringEngineeringGeographyEnvironmental ethicsEnvironmental resource managementPolitical scienceNatural (archaeology)Environmental scienceArchaeologyPolitics

Abstract

fetched live from OpenAlex

This thesis explores, through architectural design, the potential for human habitation to occur alongside a positive impact on the environment. Using water as the focal point, it will explore through architectural and landscape design how water resources for the city can be localized. Clean water can benefit the city both ecologically and socially. This design aspires to provide a sensual experience of water to benefit the public spaces of the city of Ottawa, while educating the public about responsible interaction with this fragile resource. Water is about balance. It has a dual symbolism as it is both an agent that cleans and an agent that purifies. Too much or too little water equals death for most creatures. The nature of water is sensitive and sacrificial. To clean, it must take the dirt as its own burden. The burden is now on us to preserve this precious resource;"... [we must] reflect on the bond the imagination creates between two kinds of stuff from which a city is made: urban space and urban water.” This research is about water as a landscape material, water as a resource, and how both of these aspects affect the urban condition.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.608

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.278
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes1
Has abstractyes

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