Biodiversity Indices and Medically Importance of Ticks in North Khorasan Province, Northeast of Iran
Bibliographic record
Abstract
Background: Ticks are considered as the main vectors for the transmission of various pathogens such as relapsing fever and CCHF to humans. This study was investigated the biodiversity indices and medically importance of ticks in North Khorasan Province, Northeast of Iran during 2015–2019. Methods: Specimens were captured from infested ruminants including cows, sheep, and goats. Additionally, tick collections also were performed on non-domesticated creatures such as turtles, rodents, and hedgehogs. Specimens were identified using valid identification keys. Species diversity, species richness and evenness indices have been calculated to estimate species biodiversity of ticks. Results: A total of 1478 adult ticks were collected. The specimens were from two families: Ixodidae (90.05%) and Argasidae (9.95%), 6 genera and 17 species including: Rhipicephalus sanguineus (55.9%), Rhipicephalus bursa (13.4%), Hyalomma marginatum (9.5%), Hyalomma anatolicum (9.5%), Hyalomma asiaticum (0.2%), Hyalomma aegyptium (0.5%), Hyalomma scupense (1.3%), Hyalomma sp (1.2%), Haemaphysalis sulcata (0.7%), Haemaphysalis erinacea (0.1%), Haemaphysalis inermis (0.1%), Haemaphysalis punctata (0.2%), Haemaphysalis concinna (0.1% Boophilus annulatus (1.2), and Dermacentor marginatus (6.1%) among hard ticks as well as Argas persicus (91.8%) and Argas reflexus (8.2%) amongst soft ticks. Rhipicephalus sanguineus, Rh. Bursa, Hy. marginatum and Hy. anatolicum were known as the most frequent species of hard ticks. Tick’s species richness, Shannon diversity index and Simpson index in this area were S= 17, H’= 1.69, D= 0.294 respectively. Conclusion: Based on tick distribution veterinary authority, public health organizations and other officials should act for implementation of disease prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".