Delimitation of <i>Alosa</i> species (Teleostei: Clupeiformes) from the Sea of Azov: integrating morphological and molecular approaches
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".