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Record W4210667089 · doi:10.1002/lob.10479

The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

2022· article· en· W4210667089 on OpenAlexaboutno aff
Maria Grigoratou, Enrique Montes, Anthony J. Richardson, Jason D. Everett, Esteban Acevedo‐Trejos, Clarissa R. Anderson, Bingzhang Chen, Tamar Guy‐Haim, Jana Hinners, Christian Lindemann, Tatiane Martins Garcia, Klas Ove Möller, Fanny Monteiro, Aimee Neeley, Todd O’Brien, Artur Palacz, Alex J. Poulton, A. E. Friederike Prowe, Áurea E. Rodríguez‐Santiago, Cécile S. Rousseaux, Jeffrey A. Runge, Juan Francisco Saad, Ioulia Santi, Rowena Stern, Alice Soccodato, Selina Våge, Meike Vogt, Soultana Zervoudaki, Frank Müller‐Karger

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersOffice of Naval ResearchNational Science Foundation of Sri LankaNatural Environment Research CouncilNuclear Safety and Security CommissionNational Aeronautics and Space AdministrationNorges ForskningsrådAustralian Research CouncilNational Oceanic and Atmospheric AdministrationSight Research UKNational Science Foundation
KeywordsPlanktonBiodiversityEcosystemMarine ecosystemEcologyBiologyEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

Author: Grigoratou, Maria et al.; Genre: Journal Article; Finally published : 2022; Open Access; Title: The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

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)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0030.006
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0180.011

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 head0.038
GPT teacher head0.226
Teacher spread0.188 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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