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Record W4226266272 · doi:10.1093/plankt/fbac020

Molecular and morphological analyses to improve taxonomic classification of<i>Metridia lucens</i>/<i>pacifica</i>in the North Pacific

2022· article· en· W4226266272 on OpenAlexafffund
Junya Hirai, Fang Chen, Hiroshi Itoh, Kazuaki Tadokoro, Matthew A. Lemay, Brian P. V. Hunt, Atsushi Tsuda

Bibliographic record

VenueJournal of Plankton Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of British ColumbiaTula Foundation
FundersHakai InstituteJapan Society for the Promotion of ScienceTula FoundationAsahi Glass Foundation
KeywordsBiologyTaxonomy (biology)Phylogenetic treeZoologyPacific oceanEcologyOceanographyGeneticsGeology

Abstract

fetched live from OpenAlex

Abstract Molecular and morphological analyses were used to resolve the taxonomy of Metridia lucens/pacifica in the North Pacific. Phylogenetic analysis of mitochondrial cytochrome c oxidase subunit I (mtCOI) revealed two lineages of M. lucens and M. pacifica with an average sequence difference of 13.0%, which were supported by variations in nuclear internal transcribed spacer sequences. Metridia pseudopacifica in the eastern Pacific, mentioned by Mackas and Galbraith (2002), was included in M. lucens. The presence of M. lucens and M. pacifica was confirmed across the North Pacific, and different genetic population structures were suggested between the two species in the North Pacific based on mtCOI sequences. The morphological identification character of length of setae on the fifth pair of legs was not useful for classifying adult female M. pacifica and M. lucens. There were regional variations in prosome length and head angle; these two characters successfully classified &amp;gt;98.2% of M. pacifica and M. lucens specimens from the North Pacific. As M. lucens and M. pacifica are key copepods in the North Pacific, accurate classification of individuals should be made in future studies to understand their relative contributions to marine ecosystems and improve detection of ecosystem change.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.353
Teacher spread0.237 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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