MétaCan
Menu
Back to cohort
Record W4319877921 · doi:10.15451/ec2023-02-12.05-1-14

Food Biodiversity as an Opportunity to Address the Challenge of Improving Human Diets and Food Security

2023· article· en· W4319877921 on OpenAlexaff
Michelle Cristine Medeiros Jacob, Alice Medeiros Souza, Aline Martins de Carvalho, Carlos Frederico Alves de Vasconcelos Neto, Daniel Tregidgo, Danny Hunter, Fillipe de Oliveira Pereira, Guilhermo Ros Brull, Harriet V. Kunhlein, Lara Juliane Guedes da Silva, Larissa Mont'Alverne Jucá Seabr, Mariana de Paula Drewinski, Nelson Menolli, Patrícia Carignano Torres, Pedro Mayor, Priscila F. M. Lopes, Rafael Ricardo Vasconcelos da Silva, Sávio Marcelino Gomes, Juliana Kelly da Silva-Maia

Bibliographic record

VenueEthnobiology and Conservation · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsCargill (Canada)
Fundersnot available
KeywordsFood securityBiodiversityFood systemsBusinessNatural resource economicsSustainable agricultureSustainabilityEnvironmental resource managementEnvironmental planningGeographyAgricultureBiologyEcologyEconomics

Abstract

fetched live from OpenAlex

Scientists have warned for several years that food systems have become major drivers of environmental degradation, malnutrition, and food insecurity. In this paper, we present arguments from specialists that suggest that, in the transition to more sustainable food systems, biodiversity and food security can be mutually supportive, rather than conflicting goals. We have divided the opinions of these scientists into two "Big Topics". First, they examine the synergies and challenges of the intersection of biodiversity and food security. In the second section, they explain how various forms of food biodiversity, such as mushrooms, terrestrial wild animals, aquatic animals, algae, and wild plants, can contribute to food security. Finally, we present three main pathways that, according to these experts, could guide the transition toward biodiversity and food security in food systems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.085
GPT teacher head0.279
Teacher spread0.194 · 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 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

Citations24
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEthnobiology and ConservationSame topicAgriculture and Rural Development ResearchFrench-language works237,207