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Record W4386225637 · doi:10.1038/s41558-023-01779-1

Sea-ice decline could keep zooplankton deeper for longer

2023· article· en· W4386225637 on OpenAlexafffund
Hauke Flores, Gaëlle Veyssière, Giulia Castellani, Jeremy Wilkinson, Mario Hoppmann, Michael Kärcher, Lovro Valcic, Astrid Cornils, Maxime Geoffroy, Marcel Nicolaus, Barbara Niehoff, Pierre Priou, Katrin Schmidt, Julienne Strœve

Bibliographic record

VenueNature Climate Change · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaMemorial University of Newfoundland
FundersNatural Environment Research CouncilFisheries and Oceans CanadaOcean Frontier InstituteBundesministerium für Bildung und ForschungCanada First Research Excellence FundArcticNetNorges ForskningsrådEuropean CommissionSight Research UK
KeywordsZooplanktonSea iceOceanographyArcticEnvironmental sciencePolar nightSunsetSunriseEcosystemArctic sea ice declineMarine ecosystemArctic ice packClimatologyGeographyEcologyAtmospheric sciencesGeologyDrift iceBiology

Abstract

fetched live from OpenAlex

Abstract As Arctic sea ice deteriorates, more light enters the ocean, causing largely unknown effects on the ecosystem. Using an autonomous biophysical observatory, we recorded zooplankton vertical distribution under Arctic sea ice from dusk to dawn of the polar night. Here we show that zooplankton ascend into the under-ice habitat during autumn twilight, following an isolume of 2.4 × 10 −4 W m −2 . We applied this trigger isolume to CMIP6 model outputs accounting for incoming radiation after sunset and before sunrise of the polar night. The models project that, in about three decades, the total time spent by zooplankton in the under-ice habitat could be reduced by up to one month, depending on geographic region. This will impact zooplankton winter survival, the Arctic foodweb, and carbon and nutrient fluxes. These findings highlight the importance of biological processes during the twilight periods for predicting change in high-latitude ecosystems.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

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)0.0010.001

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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2023
Admission routes2
Has abstractyes

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