New locality records for Steinernema and Heterorhabditis (Nematoda: Rhabditida: Steinernematidae, Heterorhabditidae) fauna of Ukraine
Bibliographic record
Abstract
Goal. Of this study is to present new locality records (from agricultural ecosystems) for Steinernema and Heterorhabditis (Nematoda: Rhabditida: Steinernematidae, Heterorhabditidae) fauna of Ukraine. Methods. The object of our research were entomopathogenic nematodes collected from different localities of Ukraine between 2016 and 2021. We used stereoscopic microscope MBS-9, light microscope Carl Zeiss Primo Star 100x—1000x and specialized keys for entomopathogenic nematodes identification. Results. We analyzed 312 samples for entomopathogenic nematodes (Steinernematidae, Heterorhabditidae). Entomopathogenic nematodes were isolated from Zhytomyr region, Chernihiv region and Kyiv region. Entomopathogenic nematodes from Zhytomyr region and Chernihiv region is a new record for Ukraine. EPN-positive soil samples with Steinernema spp. were noticeably pre-dominating over the Heterorhabditis spp. (ratio 1.5 to 1). Three species of entomopathogenic nematodes (S. carpocapsae, Steinernema sp. «glaseri»-group and H. bacteriophora), have been described. The information on the specimens location and brief notes on the habitats is provided. In present study, we examined differences in the morphological and morphometric characters between two EPN species from different regions/zones of Ukraine. Conclusions. We found new locality records for two entomopathogenic nematodes species: S. сarpocapsae and H. bacteriophora reported from Zhytomyr region and Chernihiv region of Ukraine for the first time. Further studies aiming to improve the knowledge on entomopathogenic nematodes (Steinernema and Heterorhabditis) fauna should focus on collecting in little-known areas and some specific habitats of Ukraine.
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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 | 0.000 |
| 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".