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Record W4318983122 · doi:10.1038/s41467-023-36241-5

Monitoring and modelling marine zooplankton in a changing climate

2023· review· en· W4318983122 on OpenAlexafffund
Lavenia Ratnarajah, Rana Abu‐Alhaija, Angus Atkinson, Sonia Batten, Nicholas J. Bax, Kim S. Bernard, Gabrielle Canonico, Astrid Cornils, Jason D. Everett, Maria Grigoratou, Nurul Huda Ahmad Ishak, David G. Johns, Fabien Lombard, Erik Muxagata, Clare Ostle, Sophie Pitois, Anthony J. Richardson, Katrin Schmidt, Lars Stemmann, Kerrie M. Swadling, Guang Yang, Lidia Yebra

Bibliographic record

VenueNature Communications · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsNorth Pacific Marine Science Organization
FundersNatural Environment Research CouncilYayasan Penyelidikan Antartika Sultan MizanConselho Nacional de Desenvolvimento Científico e TecnológicoNorth Pacific Research BoardNorth Pacific Marine Science OrganizationHorizon 2020 Framework ProgrammeCommonwealth Scientific and Industrial Research OrganisationEuropean Regional Development FundEuropean CommissionInstitut Universitaire de FranceAustralian Institute of Marine ScienceSight Research UKExxon Valdez Oil Spill Trustee CouncilNational Science Foundation
KeywordsZooplanktonEnvironmental scienceOceanographyClimate changeEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Zooplankton are major consumers of phytoplankton primary production in marine ecosystems. As such, they represent a critical link for energy and matter transfer between phytoplankton and bacterioplankton to higher trophic levels and play an important role in global biogeochemical cycles. In this Review, we discuss key responses of zooplankton to ocean warming, including shifts in phenology, range, and body size, and assess the implications to the biological carbon pump and interactions with higher trophic levels. Our synthesis highlights key knowledge gaps and geographic gaps in monitoring coverage that need to be urgently addressed. We also discuss an integrated sampling approach that combines traditional and novel techniques to improve zooplankton observation for the benefit of monitoring zooplankton populations and modelling future scenarios under global changes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.769

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.006
Research integrity0.0000.002
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 head0.102
GPT teacher head0.378
Teacher spread0.276 · 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 designNot applicable
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

Citations238
Published2023
Admission routes2
Has abstractyes

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