MétaCan
Menu
Back to cohort
Record W2905213451 · doi:10.1029/2018gl080942

Heterogeneous Changes in Western North American Glaciers Linked to Decadal Variability in Zonal Wind Strength

2018· article· en· W2905213451 on OpenAlexafffund
Brian Menounos, Romain Hugonnet, David Shean, Alex Gardner, Ian M. Howat, Étienne Berthier, Ben M. Pelto, C. Tennant, J. M. Shea, Myoung‐Jong Noh, Fanny Brun, Amaury Dehecq

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
FundersGlobal Water FuturesJet Propulsion LaboratoryCentre National d’Etudes SpatialesBC HydroAmes Research CenterTula FoundationHakai InstituteOhio State UniversityNational Aeronautics and Space AdministrationNational Park ServiceU.S. Geological SurveyUniversity of Northern British ColumbiaNational Science Foundation
KeywordsGlacierClimatologyGlacier mass balanceClimate changeGeologyTerrainElevation (ballistics)Physical geographyDigital elevation modelSea level riseSea levelEnvironmental scienceOceanographyGeographyGeomorphologyRemote sensingCartography

Abstract

fetched live from OpenAlex

Abstract Western North American (WNA) glaciers outside of Alaska cover 14,384 km 2 of mountainous terrain. No comprehensive analysis of recent mass change exists for this region. We generated over 15,000 multisensor digital elevation models from spaceborne optical imagery to provide an assessment of mass change for WNA over the period 2000–2018. These glaciers lost 117 ± 42 gigatons (Gt) of mass, which accounts for up to 0.32 ± 0.11 mm of sea level rise over the full period of study. We observe a fourfold increase in mass loss rates between 2000–2009 [−2.9 ± 3.1 Gt yr −1 ] and 2009–2018 [−12.3 ± 4.6 Gt yr −1 ], and we attribute this change to a shift in regional meteorological conditions driven by the location and strength of upper level zonal wind. Our results document decadal‐scale climate variability over WNA that will likely modulate glacier mass change in the future.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.302
Teacher spread0.262 · 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

Citations120
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicCryospheric studies and observationsFrench-language works237,207