Genetic and morphological heterogeneity within<i>Eucyclops serrulatus</i>(Fischer, 1851) (Crustacea: Copepoda: Cyclopidae)
Bibliographic record
Abstract
Numerous studies have revealed 17 species and subspecies in the species complex E. serrulatus (Fischer, 1851). As a result, it is clear now that the former cosmopolitan species in fact represent a group of closely related species. Some of them are possibly cryptic taxa and need to be redescribed. The objective of this study was to analyse the broadly distributed E. serrulatus populations in Europe and Asia: in Saint Petersburg (the type locality, Russia), Central Russia, Odessa region and Zakarpattia region – Dniester and Danube river drainage basins accordingly (Ukraine), Valencia (Spain), Oslo (Norway), Paris (France) and in Taiwan. Mitochondrial gene (CO1) and morphological analyses revealed a significant heterogeneity between and within the above-mentioned populations. Populations from Ukraine displayed 27.1% genetic differences, supported by qualitative and quantitative morphological distinctions. The heterogeneity in the CO1 gene was shown even in E. serrulatus from the Orlov Pond (Russia) – terra typica for this species. Morphological analysis did not confirm this heterogeneity. Valencian (Spain) and Taiwanese E. cf. serrulatus separate out from other populations. As a result, a new species Eucyclops taiwanensis sp. n. from Taiwan is described.http://www.zoobank.org/urn:lsid:zoobank.org:pub:237198AE-4549-4E55-A836-AAE8A039CB51
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".