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Record W4313594019 · doi:10.1016/j.isci.2023.105926

Adaptation to compound climate risks: A systematic global stocktake

2023· article· en· W4313594019 on OpenAlexafffund
Nicholas P. Simpson, Portia Adade Williams, Katharine J. Mach, Lea Berrang‐Ford, Robbert Biesbroek, Marjolijn Haasnoot, Alcade C. Segnon, Donovan Campbell, Justice Issah Musah-Surugu, Elphin Tom Joe, Abraham Marshall Nunbogu, Salma Sabour, Andreas L. S. Meyer, Talbot M. Andrews, Chandni Singh, A.R. Siders, Judy Lawrence, Maarten van Aalst, Christopher H. Trisos

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsUniversity of Waterloo
FundersConsortium of International Agricultural Research CentersInternational Development Research CentreGovernment of the United KingdomWorld Bank Group
KeywordsLivelihoodAdaptation (eye)Affect (linguistics)Climate changeFood securityClimate change adaptationRisk managementEnvironmental resource managementNatural resource economicsBusinessRisk assessmentEnvironmental planningPublic economicsGeographyEconomicsPsychologyEcologyAgricultureBiologyFinance

Abstract

fetched live from OpenAlex

This article provides a stocktake of the adaptation literature between 2013 and 2019 to better understand how adaptation responses affect risk under the particularly challenging conditions of compound climate events. Across 39 countries, 45 response types to compound hazards display anticipatory (9%), reactive (33%), and maladaptive (41%) characteristics, as well as hard (18%) and soft (68%) limits to adaptation. Low income, food insecurity, and access to institutional resources and finance are the most prominent of 23 vulnerabilities observed to negatively affect responses. Risk for food security, health, livelihoods, and economic outputs are commonly associated risks driving responses. Narrow geographical and sectoral foci of the literature highlight important conceptual, sectoral, and geographic areas for future research to better understand the way responses shape risk. When responses are integrated within climate risk assessment and management, there is greater potential to advance the urgency of response and safeguards for the most vulnerable.

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 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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.013
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.133
GPT teacher head0.321
Teacher spread0.188 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations111
Published2023
Admission routes2
Has abstractyes

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