Evidence map›Paper›PMID 33230206›Full record

ArticleScientific reports2020

Mislabelling and high mercury content hampers the efforts of market-based seafood initiatives in Peru.

Daniella Biffi, Andrea López-Mobilia, Shaleyla Kelez, Dean A Williams, Matthew M Chumchal, Molly Weinburgh

Abstract read
In one paragraph

Article in Scientific reports, 2020. 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. Review
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

6 authors.

Daniella BiffiAndrews Institute of Mathematics & Science Education, Texas Christian University, Fort Worth, 76129, USA. daniella.biffi@gmail.com.ORCID 0000-0001-6437-9855
Andrea López-MobiliaRuway, Lima, Peru.ORCID 0000-0001-5754-1316
Shaleyla KelezecOceánica, Lima, Peru.ORCID 0000-0003-3296-8721
Dean A WilliamsDepartment of Biology, College of Science and Engineering, Fort Worth, 76129, USA.ORCID 0000-0002-9001-6019
Matthew M ChumchalDepartment of Biology, College of Science and Engineering, Fort Worth, 76129, USA.ORCID 0000-0001-7160-3157
Molly WeinburghAndrews Institute of Mathematics & Science Education, Texas Christian University, Fort Worth, 76129, USA.ORCID 0000-0002-7802-2336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peru is experiencing a "gastronomic boom" that is increasing the demand for seafood. We investigated two implicit assumptions of two popular sustainable seafood consumer-based initiatives: (1) seafood is labelled correctly, and (2) the recommended species are healthy for consumers. We used DNA barcoding to determine the taxonomic identity of 449 seafood samples from markets and restaurants and analysed the concentration of total mercury (THg) in a sub-sample (271 samples) of these. We found that a third of seafood is mislabelled and that over a quarter of all samples had mercury levels above the upper limit recommended by the US EPA (300 ng/g ww). Additionally, 30% of samples were threatened and protected species. Mislabelling often occurred for economic reasons and the lack of unique common names. Mislabelled samples also had significantly higher mercury concentrations than correctly labelled samples. The "best choice" species compiled from two sustainable seafood guides had less mislabelling, and when identified correctly through DNA barcoding, had on average lower mercury than the other species. Nevertheless, some high mercury species are included in these lists. Mislabelling makes the efforts of seafood campaigns less effective as does the inclusion of threatened species and species high in mercury.

Identifiers

PMID33230206
PMCPMC7683551

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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.