MétaCan
Menu
Back to cohort
Record W4411565491 · doi:10.1002/lno.70120

Seasonally migrating zooplankton strongly enhance Southern Ocean carbon sequestration

2025· article· en· W4411565491 on OpenAlexafffund
Guang Yang, Angus Atkinson, Evgeny A. Pakhomov, Katrin Schmidt, Wei‐Lei Wang, Jennifer Freer, Geraint A. Tarling

Bibliographic record

VenueLimnology and Oceanography · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNatural Science Foundation of Fujian ProvinceNational Science Foundation
KeywordsZooplanktonCarbon sequestrationOceanographyEnvironmental scienceCarbon dioxideEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract High‐latitude zooplankton can sequester millions of tons of carbon due to their seasonal migration from the surface ocean to depth, and their respiration and mortality during overwintering. This seasonal vertical migration pump (SVMP) efficiently removes carbon but not limiting nutrients such as iron from the surface layers. However, this process is not included in Earth System Models and whole Southern Ocean estimates are still lacking. Here, we compile large datasets of Southern Ocean zooplankton biomass and physiology to estimate that the SVMP transports 65 Mt carbon annually to sequestration‐achieving depths of > 500 m. Mesozooplankton are the main agents (80%), followed by krill (14%), and salps (6%), with respiration and mortality at depth contributing a similar share. This SVMP adds greatly to existing modeled or measured estimates of Southern Ocean carbon sequestration, equating to 38–56% of particulate organic carbon flux at 500 m and 78–103% of the flux at 1000 m. Given their large biomass but projected change under polar warming, understanding how zooplankton transport carbon and nutrients will underpin improved model projections of ocean carbon storage in a warmer world.

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 score0.008
Threshold uncertainty score0.486

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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueLimnology and OceanographySame topicOcean Acidification Effects and ResponsesFrench-language works237,207