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Record W2972314639 · doi:10.14430/arctic68884

Water Vulnerability in Arctic Households: A Literature-based Analysis

2019· article· en· W2972314639 on OpenAlexvenueno aff
Antonia Sohns, James D. Ford, Mylène Riva, Brian E. Robinson, Jan Adamowski

Bibliographic record

VenueARCTIC · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)ArcticVulnerability assessmentEnvironmental resource managementGeographyClimate changeEnvironmental planningPsychological resilienceEnvironmental scienceEcologyPsychologyComputer science

Abstract

fetched live from OpenAlex

There is an urgent need to understand the contextual factors that influence water vulnerability of households in the Arctic. To evaluate the existing knowledge of Arctic household water vulnerability, this paper presents the results of a narrative review with a systematic search. The review identified 112 documents, including peer-reviewed articles, reviews, book chapters, proceedings papers, and meeting abstracts. The documents were analyzed for the main factors affecting water vulnerability in Arctic households, which fell into two categories: biophysical factors and anthropogenic factors. Within the biophysical category, the majority of documents noted climate change impacts on freshwater supplies and water systems, followed by attention to extreme weather and the seasonality of water supplies. Within the anthropogenic category, the vast majority highlighted infrastructure as the primary issue affecting water access, followed by economic, governance, socio-cultural, and demographic factors. Through these diverse influencing factors, this review situates the discussion of household water vulnerability in the Arctic in a more nuanced light. The categories illuminate patterns between factors, which can worsen, assuage, or mitigate water vulnerability. The complex relationships between these factors influence the degree and nature of water vulnerability in Arctic households. In order to successfully address household water vulnerability in the Arctic, these factors and their dynamic relationships must be considered in freshwater policy and management frameworks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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Same venueARCTICSame topicIndigenous Studies and EcologyFrench-language works237,207