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Record W4211016587 · doi:10.1016/j.dib.2022.107941

Urban sprawl in Canada: Values in all 33 Census Metropolitan Areas and corresponding 469 Census Subdivisions between 1991 and 2011

2022· article· en· W4211016587 on OpenAlexaffabout
Mehrdokht Pourali, Craig Townsend, Angela Kross, Alex Guindon, Jochen A.G. Jaeger

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsConcordia University
Fundersnot available
KeywordsUrban sprawlCensusMetropolitan areaGeographySubdivisionUnit (ring theory)Per capitaUrban planningRegional sciencePopulationDemographyCivil engineeringMathematicsEngineeringSociologyArchaeology

Abstract

fetched live from OpenAlex

The dataset presented here provides the degree of urban sprawl across 33 Census Metropolitan Areas (CMAs) in Canada of 2011 together with the 469 Census Subdivisions (CSDs) located within the 2011 boundaries of the CMAs, for the years 1991, 2001, and 2011. The dataset contains the values of weighted urban proliferation (WUP) and weighted sprawl per capita (WSPC) and their components. The landscape-oriented value of WUP indicates how strongly the landscape within the boundaries of a reporting unit is sprawled per square meter, while WSPC is inhabitant-oriented and reveals how much on average an inhabitant or workplace is contributing to urban sprawl in the reporting unit. The values of the components of the WUP and WSPC metrics are provided as well: percentage of built-up area (PBA), urban dispersion (DIS), land uptake per person (LUP), and urban permeation (UP). The values of full-time equivalents for the numbers of jobs, which were considered in the calculation of LUP values (pertaining to the number of inhabitants and jobs) are also included in order to facilitate future research.

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 score0.049
Threshold uncertainty score0.495

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 science0.0000.001
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 head0.039
GPT teacher head0.258
Teacher spread0.219 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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