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Record W1983854685 · doi:10.3197/096734002129342594

Extreme Weather and the Energy Metabolism of the City

2002· article· en· W1983854685 on OpenAlexaffabout
Raymond Murphy

Bibliographic record

VenueEnvironment and History · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMetropolitan areaStormExtreme weatherSituatedArchitectural engineeringClimate changeEnvironmental resource managementEcologyEnvironmental scienceHistoryGeographyMeteorologyComputer scienceEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract This study uses a critical realist perspective to investigate relations between social constructions and the dynamics of nature. The material metabolism of the modern city is based on the redeployment of the processes of nature. This redeployment provides energy for anabolic processes in which complex social and physical hybrids (heating, lighting, transportation, communication, water-supply systems and climate-controlled micro-environments) are built from simpler structures. Massive energy flows of nature can, however, confront the city, unleashing catabolic reactions in which complex social and physical hybrids are broken down to simpler ones. This case-study of the 1998 ice storm in north-eastern North America documents the learning that occurs as a result of nature's overwhelming energy flows destroying the essential infrastructures of modern urban life. This extreme weather event knocked out for an unusually long period the electrical transmission system that provides the energy for a metropolitan area situated in a dark, frigid environment, and thereby produced the most costly disaster in Canada's history.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.143
Teacher spread0.110 · 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

Citations9
Published2002
Admission routes2
Has abstractyes

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