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Record W2970335361 · doi:10.1130/g46507.1

Early start of 20th-century Arctic sea-ice decline recorded in Svalbard coralline algae

2019· article· en· W2970335361 on OpenAlexafffund
Steffen Hetzinger, Jochen Halfar, Zoltán Zajacz, Max Wisshak

Bibliographic record

VenueGeology · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsSea iceOceanographyArctic ice packArctic sea ice declineArcticGeologyAntarctic sea iceCryosphereClimatologyClimate changePhysical geographyGeography

Abstract

fetched live from OpenAlex

Abstract The fast decline of Arctic sea ice is a leading indicator of ongoing global climate change and is receiving substantial public and scientific attention. Projections suggest that Arctic summer sea ice may virtually disappear within the course of the next 50 or even 30 yr with rapid Arctic warming. However, limited observational records and lack of annual-resolution marine sea-ice proxies hamper the assessment of long-term changes in sea ice, leading to large uncertainties in predictions of its future evolution under global warming. Here, we use long-lived encrusting coralline algae that strongly depend on light availability as a new in situ proxy to reconstruct past variability in the duration of seasonal sea-ice cover. Our data represent the northernmost annual-resolution marine sea-ice reconstruction to date, extending to the early 19th century off Svalbard. Algal records show that the decreasing trend in sea-ice cover in the high Arctic had already started at the beginning of the 20th century, earlier than previously reported from sea-ice reconstructions based on terrestrial archives. Our data further suggest that, although sea-ice extent varies on multidecadal time scales, the lowest sea-ice values within the past 200 yr occurred at the end of the 20th century.

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 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 score0.011
Threshold uncertainty score1.000

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)0.0060.001

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 head0.008
GPT teacher head0.205
Teacher spread0.196 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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