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Record W4362681650 · doi:10.1525/elementa.2022.00130

Untangling the influence of Antarctic and Southern Ocean life on clouds

2023· article· en· W4362681650 on OpenAlexaff
Marc Mallet, Ruhi S. Humphries, Sonya Fiddes, Simon P. Alexander, Katye E. Altieri, Hélène Angot, N. Anilkumar, Thorsten Bartels‐Rausch, Jessie M. Creamean, Manuel Dall’Osto, Aurélien Dommergue, M. M. Frey, Silvia Henning, Delphine Lannuzel, Rémy Lapere, Gerald G. Mace, Anoop S. Mahajan, Greg M. McFarquhar, Klaus M Meiners, Branka Miljevic, Ilka Peeken, Alain Protat, Julia Schmale, Nadja Steiner, Karine Sellegri, Rafel Simó, Jennie L. Thomas, Megan D. Willis, V. Holly L. Winton, Matthew T. Woodhouse

Bibliographic record

VenueElementa Science of the Anthropocene · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsFisheries and Oceans Canada
FundersEuropean CommissionSight Research UKNatural Environment Research CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAustralian GovernmentRoyal Society Te ApārangiNational Computational InfrastructureNational Science Foundation
KeywordsBiogeochemical cycleSea iceClimate changeEnvironmental scienceScale (ratio)Atmosphere (unit)CryosphereOceanographyGeographyEnvironmental resource managementEarth scienceClimatologyMeteorologyGeologyEcology

Abstract

fetched live from OpenAlex

Polar environments are among the fastest changing regions on the planet. It is a crucial time to make significant improvements in our understanding of how ocean and ice biogeochemical processes are linked with the atmosphere. This is especially true over Antarctica and the Southern Ocean where observations are severely limited and the environment is far from anthropogenic influences. In this commentary, we outline major gaps in our knowledge, emerging research priorities, and upcoming opportunities and needs. We then give an overview of the large-scale measurement campaigns planned across Antarctica and the Southern Ocean in the next 5 years that will address the key issues. Until we do this, climate models will likely continue to exhibit biases in the simulated energy balance over this delicate region. Addressing these issues will require an international and interdisciplinary approach which we hope to foster and facilitate with ongoing community activities and collaborations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.007
GPT teacher head0.230
Teacher spread0.222 · 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

Citations35
Published2023
Admission routes1
Has abstractyes

Explore more

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