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Record W4407731062 · doi:10.1038/s43016-025-01143-w

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

2025· erratum· en· W4407731062 on OpenAlexaff
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
Typeerratum
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCountdownResilience (materials science)Corporate governanceBusinessEnvironmental resource managementEnvironmental planningPolitical scienceEnvironmental scienceEngineeringFinancePhysics

Abstract

fetched live from OpenAlex

In the version of the article initially published, an incorrect version of Fig. 5 was included and has now been replaced in the HTML and PDF versions of the article, as seen in Fig. 1. Fig. 1 Original and corrected Fig. 5 . Full size image

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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