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Record W1790320368 · doi:10.1017/cbo9780511783142.014

The role of urban land in climate change

2011· book-chapter· en· W1790320368 on OpenAlexaff
Hilda Blanco, Patricia L. McCarney, Susan Parnell, Marco Schmidt, Karen C. Seto

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClimate changeUrbanizationEnvironmental planningHuman settlementLand useUrban climateGovernment (linguistics)Environmental resource managementUrban planningLand managementClimate change mitigationBusinessGeographyNatural resource economicsEconomic growthEconomicsCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Recent IPCC reports have addressed the issue of urban land under the topic of industry, settlement, and society (IPCC, 2007). Since reviews of human settlements from the perspective of climate change have been primarily focused on climate change mitigation, topics of land cover and use, urbanization, land planning and management, land markets, property rights, and fiscal and legal issues, which will be key to responding to impacts of climate change, have not received extensive coverage. We argue in this chapter that it is important to focus on urban land as a sector or as the overarching framework in order to recognize the challenges of government coordination and integration necessary to address climate change. In incorporating urban land in climate change adaptation and mitigation efforts one would be able to include a fundamental set of strategies, such as policies concerning land conversion, land tenure, and urban land markets that have not been fully addressed.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.992
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.165
Teacher spread0.149 · 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 teacher head, 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

Citations43
Published2011
Admission routes1
Has abstractyes

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