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Record W4396700331 · doi:10.1080/00963402.2024.2339125

What do we really know about urban agriculture’s impact on people, places, and the planet?

2024· article· en· W4396700331 on OpenAlexaff
Agnès Fargue‐Lelièvre, Jason K. Hawes, Benjamin Goldstein, Lidia Poniży, Erica Dorr

Bibliographic record

VenueBulletin of the Atomic Scientists · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsMcGill University
FundersAgence Nationale de la RechercheNarodowe Centrum NaukiEuropean CommissionBelmont ForumEconomic and Social Research CouncilBundesministerium für Bildung und ForschungJoint Programming Initiative Urban EuropeUniverzita Karlova v PrazeUniversity of South AlabamaNarodowym Centrum NaukiNational Science Foundation
KeywordsAgriculturePlanetUrban agricultureNatural resource economicsBusinessClimate changeEnvironmental planningGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

Urban agriculture has myriad benefits for those who participate in it, but it’s not guaranteed to be more climate-friendly than conventional agriculture. That said, there are some very specific steps urban farmers can take to slash carbon emissions.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.004
GPT teacher head0.205
Teacher spread0.200 · 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
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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