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Record W7134301705

A climate change planning primer for municipal decision makers

2010· report· en· W7134301705 on OpenAlexaboutno aff
Maged Senbel, Sarah Church

Bibliographic record

VenuecIRcle (University of British Columbia) · 2010
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasClimate changeUrban planningLand useLand-use planningClimate change mitigationLand use, land-use change and forestryGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

The design of our cities and towns has a direct impact on our greenhouse gas (GHG) emissions. Local governments have the ability to respond to the challenges posed by climate change through specific planning and development practices. Strategies such as densification and mixed-use development, reduced distances between housing and employment, and improved transit, bicycle and pedestrian infrastructure all play a role in reducing GHG emissions.The configuration of land use, density, transportation networks and other relationships between buildings, roads, and infrastructure directly influence how much energy and materials we use to live, work, shop, and play. This report synthesizes research demonstrating the direct link between urban form and levels of energy consumption, with their resultant GHG emissions, and provides a number of examples of community responses to the challenge of reducing GHG emissions. The examples cover a number of Canadian communities, with a focus on B.C.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.093
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0040.002
Scholarly communication0.0090.007
Open science0.0030.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0930.032

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 head0.052
GPT teacher head0.269
Teacher spread0.218 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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