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Record W4313490263 · doi:10.1186/s12940-022-00956-7

Carbon dioxide (CO2) emissions and adherence to Mediterranean diet in an adult population: the Mediterranean diet index as a pollution level index

2023· article· en· W4313490263 on OpenAlexafffund
Silvia García, Cristina Bouzas, David Mateos, Rosario Pastor, Laura Álvarez‐Álvarez, Miguel Ángel Martínez‐González, Jordi Salas‐Salvadó, Dolores Corella, Albert Goday, J. Alfredo Martínéz, Ángel M. Alonso‐Gómez, Julia Wärnberǵ, Jesús Vioqué, Dora Romaguera, José López‐Miranda, Ramón Estruch, Francisco J. Tinahones, José Lapetra, Lluís Serra‐Majem, Blanca Riquelme‐Gallego, Xavier Pintó, José J. Gaforio, Josép Vidal, Clotilde Vázquez, Lidia Daimiel, Emilio Ros, Maira Bes‐Rastrollo, Patricia Guillem‐Sáiz, Stephanie Nishi, Robert Cabanes, Itziar Abete, Leire Goicolea‐Güemez, Enrique Gómez‐Gracia, Antonio J. Signes‐Pastor, Antoni Colom, Antoni Sureda, Sara Castro‐Barquero, José Carlos Fernández‐García, José Manuel Santos‐Lozano, José V. Sorlí, María Pascual, Olga Castañer, M. Ángeles Zulet, Jessica Vaquero‐Luna, F. Javier Basterra-Gortari, Nancy Babió, R Ciurana, Vicente Martín, Josep A. Tur

Bibliographic record

VenueEnvironmental Health · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsSt. Michael's Hospital
FundersInstituto de Salud Carlos IIICanadian Institutes of Health ResearchCentro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas AsociadasJunta de AndalucíaInstitució Catalana de Recerca i Estudis AvançatsEuropean Regional Development FundCentro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y NutriciónGeneralitat ValencianaAlmond Board of California
KeywordsMediterranean dietEnvironmental healthQuartileMedicinePopulationFood groupFood frequency questionnaireMediterranean climateGreenhouse gasCross-sectional studyOdds ratioAnimal scienceFood scienceConfidence intervalBiologyInternal medicineEcology

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.300
Teacher spread0.267 · 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 designObservational
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

Citations46
Published2023
Admission routes2
Has abstractyes

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