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Record W4412191985 · doi:10.1007/s43615-025-00651-y

3E + 3B: An Elaborated One Health Approach to Bridging the Researcher-Stakeholder Disconnect at the Food-energy-water Nexus Within Circular Bioeconomies

2025· article· en· W4412191985 on OpenAlexaff
Ashley N. Morgan, Guangqing Chi, Erika R. Gavenus, Johana Husserl, Theodore B. Henry, Charles Sims, Debra L. Miller

Bibliographic record

VenueCircular Economy and Sustainability · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsUniversity of British Columbia
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNational Science Foundation
KeywordsBridging (networking)Nexus (standard)Water energyStakeholderSociologyPolitical sciencePublic relationsEngineeringEnvironmental scienceComputer scienceWater resource management

Abstract

fetched live from OpenAlex

The food-energy-water (FEW) nexus presents a complex set of challenges as it recognizes the profound and intricate interdependence of food production, energy generation, and water resources as well as the role of people in these systems. Managing issues at this nexus and within circular bioeconomies requires a highly integrated and transdisciplinary approach to successfully frame the problem and investigate sustainable solutions. A One Health approach applies this type of systems theory at the local, regional, national, and global levels with the goal of achieving optimal health for people, animals, plants, and their shared environment. However, there often remains a disconnect between stakeholders and researchers in both the research development and research product implementation stages. To address that, we propose a complimentary framework including 3 “E”s (Earn trust, Explore fears, and Educate) and 3 “B”s (Build partnerships, develop Business models, and Bear the perceived risks) to bridge that researcher-stakeholder disconnect.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.025
GPT teacher head0.245
Teacher spread0.221 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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