Evidence map›Paper›PMID 34475799›Full record

ArticleBiodiversity data journal2021

Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning.

Gerlien Verhaegen, Emiliano Cimoli, Dhugal Lindsay

Abstract read
In one paragraph

Article in Biodiversity data journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Ctenophora: Illustrated Guide and Taxonomy.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gerlien VerhaegenAdvanced Science-Technology Research (ASTER) Program, Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan Advanced Science-Technology Research (ASTER) Program, Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Yokosuka Japan.ORCID https://orcid.org/0000-0002-8942-8112
Emiliano CimoliInstitute for Marine and Antarctic Studies, College of Sciences and Engineering, University of Tasmania, Hobart, Australia Institute for Marine and Antarctic Studies, College of Sciences and Engineering, University of Tasmania Hobart Australia.ORCID https://orcid.org/0000-0001-7964-2716
Dhugal LindsayAdvanced Science-Technology Research (ASTER) Program, Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan Advanced Science-Technology Research (ASTER) Program, Institute for Extra-cutting-edge Science and Technology Avant-garde Research (X-star), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Yokosuka Japan.ORCID https://orcid.org/0000-0002-8985-4744

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSouthern Ocean ecosystems are currently experiencing increased environmental changes and anthropogenic pressures, urging scientists to report on their biodiversity and biogeography. Two major taxonomically diverse and trophically important gelatinous zooplankton groups that have, however, stayed largely understudied until now are the cnidarian jellyfish and ctenophores. This data scarcity is predominantly due to many of these fragile, soft-bodied organisms being easily fragmented and/or destroyed with traditional net sampling methods. Progress in alternative survey methods including, for instance, optics-based methods is slowly starting to overcome these obstacles. As video annotation by human observers is both time-consuming and financially costly, machine-learning techniques should be developed for the analysis of NEW INFORMATION: In this study, we twice conducted three week-long

Indexed as

Common Objects in Context (COCO)gelatinous zooplanktonmachine learningremotely-operated vehicle (ROV)siphonophoreSouthern Oceanvideo annotation

Identifiers

PMID34475799
PMCPMC8382665

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.