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Record W3009705794 · doi:10.5343/bms.2019.0042

Paralarval and juvenile cephalopods within warm-core eddies in the North Atlantic

2020· article· en· W3009705794 on OpenAlexaboutno aff
Morag Taite, Michael Vecchione, Sheena Fennell, A. Louise Allcock

Bibliographic record

VenueBulletin of Marine Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsDNA barcodingBiologyPelagic zoneAbundance (ecology)OceanographyEcologyJuvenileFisheryZoologyGeology

Abstract

fetched live from OpenAlex

Many descriptions of paralarval and juvenile cephalopods are poor. By using DNA barcoding, a global bioidentification system for animals, along with morphological investigation, we can confirm species identifications. We have a better chance of eliminating misidentifications and, therefore, documenting the correct abundance and distribution of cephalopods within an area by combining morphological and molecular evidence. The central objectives of this study are to: (1) compare morphological vs molecular identification of cephalopods and (2) determine the occurrence of cephalopods within the deep scattering layer (DSL) within warm core eddies. The specimens reported here were collected between 2014 and 2016 during three transatlantic cruises from Galway, Ireland to St John's, Newfoundland, with a focus on assemblages in warm-core mesoscale eddies on the western part of the transect. Samples were collected from the DSL at multiple stations across mesoscale eddies. In total, 301 cephalopods belonging to 29 species were collected. Not only does our study increase the knowledge of abundance and diversity of pelagic cephalopods in this area, but it also provides sequences for species for which no comparative sequences were previously available. By examining the match/mismatch between morphological and molecular identifications, we highlight a need for revisions in some taxonomic groupings such as the family Cranchiidae.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.022
GPT teacher head0.220
Teacher spread0.197 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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