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Record W2898526376 · doi:10.1111/jfb.13847

Delimitation of <i>Alosa</i> species (Teleostei: Clupeiformes) from the Sea of Azov: integrating morphological and molecular approaches

2018· article· en· W2898526376 on OpenAlexaff
Oksana Vernygora, Corey S. Davis, Alison M. Murray, Felix A. H. Sperling

Bibliographic record

VenueJournal of Fish Biology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyAlosaTeleosteiMitochondrial DNAActinopterygiiLineage (genetic)ZoologyEvolutionary biologyPhylogenetic treeGenusPhylogeographyFisheryEcologyFish migrationFish <Actinopterygii>GeneticsGene

Abstract

fetched live from OpenAlex

Shads of the genus Alosa are essential to commercial fisheries across North America and Europe, but in some areas their species boundaries remain controversial. Traditional morphology-based taxonomy of Alosa spp. has relied heavily on the number of gill rakers and body proportions, but these can be highly variable. We use mitochondrial (mt)DNA (coI and cytb) and genome-wide single nucleotide polymorphisms (SNP) along with morphological characters to assess differentiation among endemic Ponto-Caspian shads in the Sea of Azov. Morphological species assignments based on gill-raker number were not congruent with genetic lineages shown by mtDNA and SNPs. Iterative analysis revealed that genetic lineages were associated with sampling location and several other morphometric traits (caudal peduncle depth, pre-anal length and head length). Phylogenetic analysis of the genus placed Ponto-Caspian Alosa spp. in the same evolutionary lineage as endangered Alosa spp. endemic to Greece, highlighting the importance of these findings to conservation management. We conclude that gill-raker number is not reliable for delimiting species of Alosa. This taxonomic uncertainty should be addressed by examining type material to provide a robust integrative classification for these commercially important fishes.

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 score0.024
Threshold uncertainty score0.217

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 head0.051
GPT teacher head0.268
Teacher spread0.217 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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