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Record W4285044454 · doi:10.22215/etd/2022-15080

A Golden Green Belt: Integrating Nature in Ottawa’s Next Suburbs

2022· dissertation· en· W4285044454 on OpenAlexaffabout
Yana Kigel

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsUrban sprawlWildlifeFaunaHuman settlementGeographyGreen beltSettlement (finance)Natural (archaeology)Point (geometry)PopulationHabitatArchaeologyEnvironmental ethicsSociologyEnvironmental planningEcologyUrban planningEngineeringCivil engineeringDemographyComputer science

Abstract

fetched live from OpenAlex

The greenspaces, parks, forests, yards and gardens of modern-day Ottawa's suburbs are conceived from the point of view of their human users but not as welcoming extensions of natural habitats for local fauna.An unbuilt greenspace in Nepean located at the westernmost point of contact between Ottawa's agricultural "Goldbelt" and its famous protected "Greenbelt" o ers a lens to rethink human settlement from the point of view of animals.The design of a fauna-oriented retirement campus o ers a footing for this re-oriented design methodology.Analytical design at various scales aims to discover a point of balance between private spaces and spaces of interaction for both human and animal dwellers of the site.The design of a Golden Green Belt o ers itself as a prototype for future suburban developments at a time when urban sprawl and population growth continue to alter the lands around Ottawa.The imagined neighbourhood of Golden Green Belt o ers itself as an essential link and safe wildlife passageway just as it listens to and honours the muted voices of animals amidst our human noises.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0050.002
Open science0.0010.004
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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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
Published2022
Admission routes2
Has abstractyes

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