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Record W2138762061 · doi:10.1007/s13280-014-0509-8

Urban Ecosystem Services for Resilience Planning and Management in New York City

2014· review· en· W2138762061 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

VenueAMBIO · 2014
Typereview
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsDepartment of Environment and Conservation
FundersNew York Sea Grant, State University of New York
KeywordsEcosystem servicesProvisioningRecreationBusinessEnvironmental resource managementResilience (materials science)Environmental planningContext (archaeology)Urban ecosystemEcosystem managementScale (ratio)Urban planningEcosystemEnvironmental scienceGeographyEcologyComputer science

Abstract

fetched live from OpenAlex

We review the current state of knowledge about urban ecosystem services in New York City (NYC) and how these services are regulated, planned for, and managed. Focusing on ecosystem services that have presented challenges in NYC-including stormwater quality enhancement and flood control, drinking water quality, food provisioning and recreation-we find that mismatches between the scale of production and scale of management occur where service provision is insufficient. Adequate production of locally produced services and services which are more accessible when produced locally is challenging in the context of dense urban development that is characteristic of NYC. Management approaches are needed to address scale mismatches in the production and consumption of ecosystem services. By coordinating along multiple scales of management and promoting best management practices, urban leaders have an opportunity to ensure that nature and ecosystem processes are protected in cities to support the delivery of fundamental urban ecosystem services.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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