Evidence map›Paper›PMID 35999848›Full record

ArticlePeerJ2022

Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps.

Bjørn Tore Kopperud, Scott Lidgard, Lee Hsiang Liow

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Bjørn Tore KopperudNatural History Museum, University of Oslo, Oslo, Norway.
Scott LidgardNegaunee Integrative Research Center, Field Museum of Natural History, Chicago, Illinois, U.S.A.ORCID 0000-0002-0446-4705
Lee Hsiang LiowNatural History Museum, University of Oslo, Oslo, Norway.ORCID 0000-0002-3732-6069

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences (TMO)) in an automated procedure. We compare these results with data combined from two major public databases (DB): the Ocean Biodiversity Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches. Our combined DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. A total of 79% of the georeferenced species we retrieved from TMO (

Indexed as

BiodiversityBryozoaAnimalsInformation Storage and RetrievalBimodalityBryozoaGeographic distributionLatitudinal diversity gradient (LDG)Marine invertebratesNatural langauge processing (NLP)Public data repositoriesSpecies richnessText-mining

Identifiers

PMID35999848
PMCPMC9393005

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.