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Record W4376645811 · doi:10.3389/fclim.2023.1177025

The climate-biodiversity-health nexus: a framework for integrated community sustainability planning in the Anthropocene

2023· article· en· W4376645811 on OpenAlexafffund
Robert Newell

Bibliographic record

VenueFrontiers in Climate · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsRoyal Roads University
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsRoyal Roads University
KeywordsNexus (standard)SustainabilityEnvironmental resource managementEnvironmental planningOperationalizationBusinessPolitical scienceGeographyEngineeringEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Integrated approaches to planning and policy are important for making progress toward sustainability. A variety of frameworks have been developed for facilitating such approaches to planning and policy, such as the water-energy-food (WEF) nexus. However, the WEF nexus has been criticized for a lack of clarity in how to apply the framework, whereas a goals-oriented framework potentially could be more easily applied and operationalized. This paper proposes such a framework, referred to here as the climate-biodiversity-health (CBH) nexus. The paper details the features of the CBH nexus framework, the interactions among its domains, and its potential applications. The CBH nexus consists of three domains (i.e., climate action, biodiversity conservation, and community health) and six subdomains (i.e., climate change mitigation, climate change adaptation, habitat protection and regeneration, wildlife health and welfare, physical health, and mental health). The framework can be applied in practice to develop checklists/toolkits for guiding new development and as a basis for creating community indicator systems. It can also be applied in research to identify gaps in planning and policy documents and as a lens for participatory modeling exercises. Continued experimentation with, and improvement of, the CBH framework will reveal its most useful applications, thereby opening new opportunities for communities to effectively develop and implement integrated sustainability plans and policies.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.512
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.034
GPT teacher head0.313
Teacher spread0.280 · 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

Citations18
Published2023
Admission routes2
Has abstractyes

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