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Record W4255089346 · doi:10.22498/pages.27.2.71

Cycle of Sea Ice Dynamics in the Earth System working group

2019· article· en· W4255089346 on OpenAlexaff
Amy Leventer, Karen E. Kohfeld, Claire S. Allen, Xavier Crosta, Alice Marzocchi, Joseph G. Prebble

Bibliographic record

VenuePast Global Change Magazine · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeologyGroup (periodic table)Sea iceEarth system scienceEarth (classical element)OceanographyMathematicsChemistry

Abstract

fetched live from OpenAlex

Sea ice is a critical component of the climate system (Fig. Given the high albedo of sea ice relative to that of open water, seasonal variability in sea-ice extent impacts reflectivity of the Earth's surface. Changes in the Antarctic sea-ice seasonal cycle also affect Southern Ocean water mass buoyancy, thereby modulating Southern Ocean upper and lower overturning cells and global ocean circulation. Through its interactions with the glacial ice margin, sea ice affects, and is affected by, ice sheets and ice shelves. Finally, sea-ice decay impacts ocean primary productivity through its roles in seeding phytoplankton and driving upperocean stratification and nutrient distribution (e.g. Armand et al. 2017). At present, average Antarctic sea-ice extent ranges between summer minima of ~3 million km 2 to winter maxima of ~18 million km 2 (Cavalieri and Parkinson 2008). Changes in sea-ice extent over longer time periods have been studied since the late 1970s. Communitybased research efforts have focused on understanding different sea-ice proxies (e.g. the PAGES Sea Ice Proxies working group: pastglobalchanges.org/science/wg/former/ sea-ice-proxies), and on reconstructing Antarctic sea-ice extent for specific time periods, such as

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.800

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)0.0000.000

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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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