SPECIES COMPOSITION OF WOLF (CANIS LUPUS) HELMINTH FAUNA IN KAZAKHSTAN
Bibliographic record
Abstract
The article presents data on the study of the species composition of wolf helminth fauna in the period from 2019 to 2021. A total of 39 wolves (Canis lupus) were examined, and their parasite fauna was assessed. The study identified the following types of helminths Echinococcus granulosus, Taenia krabbei, Dypilidium spp., Mesocestoides spp., Toxascaris leonina, Trichinella nativa, Dirofilaria repens. The extensiveness of the invasion wolves by helminths was relatively high in the western part region (96.5%), and towards the north-central part region, this indicator significantly decreased (65.2%). The average number of helminths was high, with some infected animals carrying several types of parasites. Thus, the intensity of invasion by helminths in wolves was 7.6 specimens per infected individual host. The research aimed to study the endoparasitic fauna of wild wolves using complex classical parasitological and molecular genetic methods. Samples of helminths were differentiated by amplification and sequencing using the marker gene cytochrome oxidase COX1 (GenBank accession number: MT877205, MZ505444, MZ669895, MZ724175). There is a need to assess the distribution of helminths in intermediate and final hosts populations and provide appropriate health education to avoid maintaining parasite life cycles and infestations, which will become a public health problem in the future.
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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 | 0.000 | 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) | 0.000 | 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".