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Record W3205700365 · doi:10.1525/elementa.2021.00007

Climate change impacts on sea-ice ecosystems and associated ecosystem services

2021· article· en· W3205700365 on OpenAlexafffund
Nadja Steiner, Jeff S. Bowman, Karley Campbell, Melissa Chierici, Eeva Eronen‐Rasimus, Marianne Falardeau, Hauke Flores, Agneta Fransson, Helena Herr, Stephen J. Insley, Hanna M. Kauko, Delphine Lannuzel, Lisa L. Loseto, Amanda Lynnes, Andy Majewski, Klaus M Meiners, Lisa A. Miller, Loïc Michel, Sébastien Moreau, Melissa Nacke, Daïki Nomura, Letizia Tedesco, J.A. van Franeker, Maria A. van Leeuwe, Pat Wongpan

Bibliographic record

VenueElementa Science of the Anthropocene · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of ManitobaWildlife Conservation Society CanadaUniversité LavalUniversity of VictoriaEnvironment and Climate Change CanadaFisheries and Oceans Canada
FundersInternational Arctic Science CommitteeNederlandse Organisatie voor Wetenschappelijk OnderzoekUK Research and InnovationMinisterie van Landbouw, Natuur en VoedselkwaliteitMarine Environmental Observation Prediction and Response NetworkBundesministerium für Bildung und ForschungNatural Environment Research CouncilAustralian GovernmentFisheries and Oceans CanadaGenome Canada
KeywordsEcosystem servicesSea iceEcosystemMarine ecosystemEnvironmental scienceClimate changeEcologyOceanographyBiology

Abstract

fetched live from OpenAlex

A rigorous synthesis of the sea-ice ecosystem and linked ecosystem services highlights that the sea-ice ecosystem supports all 4 ecosystem service categories, that sea-ice ecosystems meet the criteria for ecologically or biologically significant marine areas, that global emissions driving climate change are directly linked to the demise of sea-ice ecosystems and its ecosystem services, and that the sea-ice ecosystem deserves specific attention in the evaluation of marine protected area planning. The synthesis outlines (1) supporting services, provided in form of habitat, including feeding grounds and nurseries for microbes, meiofauna, fish, birds and mammals (particularly the key species Arctic cod, Boreogadus saida, and Antarctic krill, Euphausia superba, which are tightly linked to the sea-ice ecosystem and transfer carbon from sea-ice primary producers to higher trophic level fish, mammal species and humans); (2) provisioning services through harvesting and medicinal and genetic resources; (3) cultural services through Indigenous and local knowledge systems, cultural identity and spirituality, and via cultural activities, tourism and research; (4) (climate) regulating services through light regulation, the production of biogenic aerosols, halogen oxidation and the release or uptake of greenhouse gases, for example, carbon dioxide. The ongoing changes in the polar regions have strong impacts on sea-ice ecosystems and associated ecosystem services. While the response of sea-ice–associated primary production to environmental change is regionally variable, the effect on ice-associated mammals and birds is predominantly negative, subsequently impacting human harvesting and cultural services in both polar regions. Conservation can help protect some species and functions. However, the key mitigation measure that can slow the transition to a strictly seasonal ice cover in the Arctic Ocean, reduce the overall loss of sea-ice habitats from the ocean, and thus preserve the unique ecosystem services provided by sea ice and their contributions to human well-being is a reduction in carbon emissions.

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)0.001
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: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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
GenreReview

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

Citations108
Published2021
Admission routes2
Has abstractyes

Explore more

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