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Record W4415586516 · doi:10.21083/crrf.v29i1.7669

TheCase forUsing GreenInfrastructure (GI) in a Land Use Planning Framework for ResilientRural Communities

2025· article· W4415586516 on OpenAlex
Paul Kraehling

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSustainabilityGreen infrastructureLand useLand-use planningVariety (cybernetics)Corporate governanceRural areaRegional planningSpatial planning

Abstract

fetched live from OpenAlex

The presentation will focus on recent PhD research on the topic of using GI as a foundational land use planning tool to address rural community challenges and build rural land resiliency. For definition purposes, GI is meant here to include a broad spectrum of nature/natural elements that together provide a solid foundation for sustainability planning to communities, whether human or natural. Facets of multi-functionality and holistic use of a variety of land uses comprising GI are considered. Landscape features, ranging in scale from individual properties to large landscape areas are considered: private yards, natural areas including water features and woodlands, open space/recreation areas, working lands including agricultural fields. The focus of research is within Ontario’s planning governance system, with consideration to the differing geographic and situational circumstances of rural places across southern Ontario.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.917

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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