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Record W7007683237

Additional Dwelling Area (ADU) Estimation in GIS: Application to the City of Windsor, Ontario, Canada, Lightning Talk (7 min)

2021· article· en· W7007683237 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWindsorEstimationCategorical variableDilemmaUnit (ring theory)Residential area
DOInot available

Abstract

fetched live from OpenAlex

Acquiring a home in Canada, whether through buying or renting, has been financially challenging in recent years. Therefore, introducing a second residential unit, also known as additional dwelling unit (ADU), could be seen as a potential solution to this dilemma by providing more affordable housing for many. In this project, a model was develop using the ModelBuilder in ArcGIS to estimate the available area for an ADU on each land parcel in the City of Windsor in Ontario, Canada. Then, a categorical model was used to determine if a specific ADU complies with the constraints put on by the city. In general, more than 94% of the residential parcel in the City of Windsor have enough area to house an ADU. Among them, about 44% are eligible to have a detached ADU, while more than 40% are eligible to have an attached or interior ADUs.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.242
Teacher spread0.205 · 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