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Record W3121640914 · doi:10.1007/s00382-021-05642-x

Late twentieth century increase in northern Spitsbergen (Svalbard) glacier-derived runoff tracked by coralline algal Ba/Ca ratios

2021· article· en· W3121640914 on OpenAlexafffund
Steffen Hetzinger, Jochen Halfar, Zoltán Zajacz, Marco Möller, Max Wisshak

Bibliographic record

VenueClimate Dynamics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsGlacierSurface runoffMeltwaterArcticGeologyOceanographyCryospherePhysical geographyEnvironmental scienceClimatologySea iceGeographyEcologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract The Arctic cryosphere is changing rapidly due to global warming. Northern Svalbard is a warming hotspot with a temperature rise of ~ 6 °C over the last three decades. Concurrently, modelled data suggest a marked increase in glacier runoff during recent decades in northern Svalbard, and runoff is projected to increase. However, observational data from before anthropogenic influence are sparse and the potential effects on the surface ocean are unclear. Here, we present a 200-year record of Ba/Ca ratios measured in annual increment-forming coralline algae from northern Spitsbergen as a proxy for past glacier-derived meltwater input. Our record shows a significant increasing trend in algal Ba/Ca ratios from the late-1980s onwards matching modelled regional runoff data, suggesting a drastic increase in land-based runoff. The rate of increase is unprecedented during the last two centuries and captures the impact of amplified warming on the coastal surface ocean in the high Arctic. The algal Ba/Ca runoff proxy offers an opportunity to reconstruct past land-based runoff variability in Arctic settings in high resolution, providing important data for validating and improving climate modelling studies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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.

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

Citations12
Published2021
Admission routes2
Has abstractyes

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