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Record W4365514438 · doi:10.1017/aog.2023.14

Contribution of glaciers to water, energy and food security in mountain regions: current perspectives and future priorities

2022· article· en· W4365514438 on OpenAlexaff
Caroline Clason, Sally Rangecroft, Philip N. Owens, Edyta Łokas, Giovanni Baccolo, Nick Selmes, Dylan Beard, Jessica Kitch, Rosa María Dextre, Sergio Morera, William Blake

Bibliographic record

VenueAnnals of Glaciology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Environment Research CouncilConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaGlobal Challenges Research FundSight Research UKQuaternary Research AssociationNewton Fund
KeywordsMeltwaterGlacierGlacial periodWater securityResource (disambiguation)Climate changeFood securitySedimentEnvironmental scienceEnvironmental resource managementHydrology (agriculture)Water resourcesGeologyWater resource managementPhysical geographyGeographyOceanographyGeomorphologyEcologyComputer science

Abstract

fetched live from OpenAlex

Abstract Mountain glaciers are crucial sources of fresh water, contributing directly and indirectly to water, energy and food supplies for hundreds of millions of people. Assessing the impact of diminishing glacial meltwater contributions to the security of this resource is critical as we seek to manage and adapt to changing freshwater dynamics in a warming world. Both water quantity and quality influence water (in)security, so understanding the fluxes of water, sediment and contaminants through glacial and proglacial systems is required for holistic assessment of meltwater contribution to downstream resource security. In this paper we consider the socio-environmental role of and pressures on glacier-fed waters, discuss key research priorities for the assessment of both the quantity and quality of meltwater and reflect on the importance of situating our understanding within a transdisciplinary and inclusive research landscape.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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 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

Citations45
Published2022
Admission routes1
Has abstractyes

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