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Record W4406374711 · doi:10.1038/s43016-024-01109-4

Governance and resilience as entry points for transforming food systems in the countdown to 2030

2025· article· en· W4406374711 on OpenAlexafffund
Kate Schneider, Roseline Remans, Tesfaye Hailu Bekele, Destan Aytekin, Piero Conforti, Shouro Dasgupta, Fabrice DeClerck, Deviana Dewi, Carola Fabi, Jessica A. Gephart, Yuta J. Masuda, Rebecca McLaren, Michaela Saisana, Nancy Aburto, Ramya Ambikapathi, Mariela Almeida Rodríguez, Sı́món Barquera, Jane Battersby, Ty Beal, Christophe Béné, Carlo Cafiero, Christine Campeau, Patrick Caron, Andrea Cattaneo, Jeroen Candel, Namukolo Covic, Inmaculada del Pino Alvarez, Ismahane Elouafi, Tyler J. Frazier, Alexander K. Fremier, Pat Foley, Christopher D. Golden, Carlos González Fischer, Alejandro Guarín, Sheryl L. Hendriks, Anna Herforth, Maddalena Honorati, Jikun Huang, Yonas Getaneh, Gina Kennedy, Amos Laar, Rattan Lal, Preetmoninder Lidder, Getachew Legese Feye, Brent Loken, Hazel Malapit, Quinn Marshall, Kalkidan Ayele Mulatu, Ana Munguía, Stella Nordhagen, Danielle Resnick, Diana Suhardiman, U. Rashid Sumaila, Bingwei Sun, Belay Terefe Mengesha, Máximo Torero Cullen, Francesco N. Tubiello, Corné van Dooren, José Luis Vivero Pol, Patrick Webb, Keith Wiebe, Lawrence Haddad, Mario Herrero, José Rosero Moncayo, Jessica Fanzo

Bibliographic record

VenueNature Food · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of British Columbia
FundersWorld Cancer Research FundGlobal Affairs CanadaIrish AidEuropean CommissionGovernment of CanadaMinisterie van Buitenlandse ZakenEidgenössisches Departement für Auswärtige AngelegenheitenBundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung
KeywordsResilience (materials science)CountdownCorporate governanceBusinessEnvironmental resource managementEnvironmental planningGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0060.007
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.227
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations43
Published2025
Admission routes2
Has abstractyes

Explore more

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