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Record W2991502770 · doi:10.1111/tgis.12597

Home range and habitat: Using platial characteristics to define urban areas from the bottom up

2019· article· en· W2991502770 on OpenAlexaff
Albert Acedo, Peter A. Johnson

Bibliographic record

VenueTransactions in GIS · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHabitatOperationalizationGeographyRange (aeronautics)Context (archaeology)Representation (politics)Home rangeEcologyEpistemologyBiologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The spatial representation of a city is typically formed by top‐down jurisdictional boundaries. A parallel approach would be to consider representing a city based on platial characteristics, that is, a bottom‐up landscape created through individual and collectively derived representations. This study contributes to this discourse through the exploratory examination of the ecology notions of home range and habitat applied to humans in an urban context. Using spatial data collected through a WebGIS platform, we employ a spatial definition of sense of place and social capital to understand the platial nature of the city and, simultaneously, defining home range and habitat as platial notions. We found spatial variability among individual home range and habitat and the difficulty of traditional administrative boundaries to represent these areas. This research defines and presents home range and habitat to partially describe the emergent nature of platial theory and explores their operationalization at the urban level.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.006
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.211
Teacher spread0.198 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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