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Record W4213365817 · doi:10.1177/10780874221080145

Case Studies of Urban Metabolism: What Should be Addressed Next?

2022· article· en· W4213365817 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUrban Affairs Review · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScale (ratio)Urban metabolismField (mathematics)Regional scienceEnvironmental planningUrban planningData scienceComputer scienceManagement scienceGeographyEngineeringUrban densityCartography

Abstract

fetched live from OpenAlex

This paper analyzes case studies of Urban Metabolism (UM), an interdisciplinary field that studies the flow of materials and energy in cities. It focuses on global cases to help researchers identify research gaps. I have categorized the studies based on location, scale, and urban system. Two findings need to be specified: first, the geographic distribution of UM case studies is uneven. Only limited studies have been developed for emerging African cities despite expected large future populations. Second, neighborhood-scale cases do not use an appropriate local scale, primarily due to the lack of reliable data sources. Upon noticing concerns over (1) the evaluation of optimized metabolisms, (2) the effectiveness of knowledge transfer, and (3) the awareness of timeframe in delivering practical policy, researchers may now focus on developing more applicable planning and design guidelines while paying attention to the early communication of UM assessment results between scientists and practitioners.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.090
GPT teacher head0.305
Teacher spread0.215 · 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