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Record W2062550203 · doi:10.1029/2011eo130006

A Pan‐Arctic Network to Study Past, Present, and Future Sea Ice Trends

2011· article· en· W2062550203 on OpenAlexaffabout
Carolyn Wegner, Karen E. Frey, Christine Michel

Bibliographic record

VenueEos · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsArcticOceanographySea iceArctic ice packClimate changeArctic ecologyMarine geologyArctic geoengineeringMarine ecosystemGeographyArctic vegetationEnvironmental scienceEcosystemPhysical geographyClimatologyGeologyEcologyAntarctic sea ice

Abstract

fetched live from OpenAlex

Arctic in Rapid Transition Implementation Workshop; Winnipeg, Manitoba, Canada, 18–20 October 2010 ; Rapid transitions in Arctic sea ice and the associated global integrated Earth system impacts and socioeconomic consequences have brought the Arctic Ocean to the top of national and international geophysical and political agendas. Alarmingly, there is a persistent mismatch between observed and predicted patterns, which speaks to the complexity of planning adaptation and mitigation activities in the Arctic. Predicting future conditions of Arctic marine ecosystems for climate change requires interdisciplinary and pan‐Arctic characterization and understanding of past and present trends. The Arctic in Rapid Transition (ART) initiative is an integrative, international, interdisciplinary, pan‐Arctic network to study spatial and temporal changes in sea ice cover and ocean circulation over broad time scales to better understand and forecast the impact of these changes on Arctic marine ecosystems and biogeochemistry. The ART initiative began in October 2008 and is still led by early‐career scientists. The ART science plan, developed after the ART initiation workshop in November 2009, was endorsed by the Arctic Ocean Sciences Board, which is now the Marine Working Group of the International Arctic Science Committee.

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 score0.199
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 head0.017
GPT teacher head0.215
Teacher spread0.198 · 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

Citations1
Published2011
Admission routes2
Has abstractyes

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