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Record W2612497353 · doi:10.1111/conl.12359

Unsupported Conclusions on Net Conservation Benefits of Mislabeling Seafood

2017· article· en· W2612497353 on OpenAlexaff
Kimberly Warner, Dana Miller, Amanda M. Naaum

Bibliographic record

VenueConservation Letters · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIUCN Red ListRanking (information retrieval)Conservation statusVaguenessFisheryBiologyGeographyStatisticsEcologyComputer scienceMathematicsInformation retrievalArtificial intelligence

Abstract

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Stawitz et al. have attempted to quantifiably address the impacts of seafood mislabeling in a powerful statistical fashion, an admirable goal. However, their conclusion that mislabeling increases conservation status of consumed items is not well supported. Stawitz et al.’s (2016) analyses and conclusions on conservation were particularly troublesome given their nontransparent methods. The authors did not reveal which 43 studies of the 45 listed in Table S1 met their criteria for meta-analyses, nor did they explain why they included 5,200 non-mislabeled samples in their analyses. They did not provide the proportion of the approximately 1,553 (i.e., 6,754/0.23) “true id” or “labeled” samples subjected to estimated IUCN conservation status nor explain how the sizable proportion of aquacultured species were treated. No reasoning was given for why they performed their own de novo investigation of mislabeling globally using U.S. guidance on seafood market names (figure S1). Given the vagaries of seafood labeling globally, we are not convinced that quantifying the conservation net benefits of mislabeling is an appropriate research question. The authors’ IUCN averaging method ignores the conservation and health implications of vaguely labeled seafood (e.g., Lowenstein et al. 2010; Lamedin et al. 2015) and prevents robust comparisons of conservation status between labeled and true id samples. We envision a number of potential erroneous conclusions based on the vagueness of the label, the nonspecificity of the true id, and the contrasting conservation ranking of disparate species within one genus (e.g., Thunnus, Supplementary Table provided). The incongruity of the Food and Agriculture Organization of the United Nations (FAO) and RAM Legacy Stock Assessment Database (RAM) database results (supp. S7 & S8) further weakens conclusions based on presumed IUCN status changes by showing larger numbers of true id genera that change status, and in opposite directions compared to IUCN trends (e.g., grouper, flounder, and Atlantic salmon). We worry most how conservation managers may respond to these and other unsupported conclusions, such as which points in the supply chain and genera to target to reduce mislabeling. To focus on “…points in the chain-of custody beyond ports, where the majority of mislabeling occurred” is not supported by the data analyzed. Although more studies may have sampled at the retail level, the mislabeling detected could have happened at any point upstream of this level, including at ports. Likewise, how could managers select genera most prone to mislabeling when both labeled and true id genera are mixed in their analyses (figure 3)? If anything, this argues for tracing all seafood. Even if all their methods were robust and valid, which we argue are not, a less threatened substitute species sold as a marginally more threatened one does not remove the market DEMAND for the more threatened species; neither does it negate the need for accurate labeling for stock assessment purposes. The authors also did not adequately address how mislabeling impacts consumer perceptions of seafood sustainability or how mislabeling can facilitate illegal fishing. These erroneous conclusions may be used to support reduced regulatory focus on seafood mislabeling, ignoring the issue's very real complexities and conservation implications. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.289
Teacher spread0.246 · 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 designBench or experimental
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

Citations6
Published2017
Admission routes1
Has abstractyes

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