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Record W2155689594 · doi:10.1029/2005eo340001

Arctic system on trajectory to new, seasonally ice‐free state

2005· article· en· W2155689594 on OpenAlexaff
Jonathan T. Overpeck, Matthew Sturm, Jennifer A. Francis, Donald K. Perovich, Mark C. Serreze, Ronald Benner, Eddy C. Carmack, F. Stuart Chapin, S. Craig Gerlach, Lawrence C. Hamilton, L. D. Hinzman, Marika M. Holland, Henry P. Huntington, Jeffrey R. Key, Andrea H. Lloyd, Glen M. McDonald, Joe McFadden, David Noone, Terry D. Prowse, Peter Schlösser, Charles J Vörösmarty

Bibliographic record

VenueEos · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
FundersDivision of Arctic SciencesGoddard Space Flight CenterNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsInterglacialArcticArctic ecologyClimatologyGlacial periodArctic geoengineeringArctic ice packArctic dipole anomalyEnvironmental scienceClimate changeThe arcticGlobal warmingIce-albedo feedbackOceanographyGeologyDrift icePaleontology

Abstract

fetched live from OpenAlex

The Arctic system is moving toward a new state that falls outside the envelope of glacial‐interglacial fluctuations that prevailed during recent Earth history. This future Arctic is likely to have dramatically less permanent ice than exists at present. At the present rate of change, a summer ice‐free Arctic Ocean within a century is a real possibility, a state not witnessed for at least a million years. The change appears to be driven largely by feedback‐enhanced global climate warming, and there seem to be few, if any processes or feedbacks within the Arctic system that are capable of altering the trajectory toward this “super interglacial” state.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations157
Published2005
Admission routes1
Has abstractyes

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