MétaCan
Menu
Back to cohort
Record W4393037210 · doi:10.1007/s00550-024-00533-1

Advancing the resource nexus concept for research and practice

2024· article· en· W4393037210 on OpenAlexaff
Floor Brouwer, Serena Caucci, Daniel Karthe, Sabrina Kirschke, Kaveh Madani, Andréa Mueller, Lulu Zhang, Edeltraud Guenther

Bibliographic record

VenueSustainability Nexus Forum · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
FundersSächsisches Staatsministerium für Wissenschaft und KunstBundesministerium für Bildung und Forschung
KeywordsNexus (standard)InterdependenceConstruct (python library)Resource (disambiguation)Knowledge managementManagement scienceComputer scienceSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Abstract The nexus concept has considerably matured during the past decade. Numerous literature reviews have significantly contributed to taking stock of the advancements in knowledge and tool development to improve science-policy support on highly connected and interdependent resources. However, literature reviews often focus on specific sector-based nexus concepts (such as water-energy-food nexus) and analyses (such as environmental assessment, technical tools, or the management and policy dimension). Therefore, a comprehensive understanding of the actual nexus and the resources it builds upon still needs to be improved. This paper aims to test the validity of the nexus construct for research and practice. Based on a systematic review of reviews, including 62 nexus-related review papers and subsequent consultation of some sixty nexus experts, we suggest a robust but flexible approach to advancing the Resource Nexus for research and practice. In doing so, the knowledge provided by nexus research may provide more substantial support to decision-makers when designing and implementing policies for the sustainable management of environmental resources.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.350
Teacher spread0.326 · 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 designTheoretical or conceptual
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

Citations48
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSustainability Nexus ForumSame topicWater-Energy-Food Nexus StudiesFrench-language works237,207