Bacteria related to tick-borne pathogen assemblages in Ornithodoros cf. hasei (Acari: Argasidae) and blood of the wild mammal hosts in the Orinoquia region, Colombia
Bibliographic record
Abstract
Interest in research on soft ticks has increased in recent decades, leading to valuable insight into their role as disease vectors. The use of metagenomics-based analyses have helped to elucidate ecological factors involved in pathogen, vector, and host dynamics. To understand the main bacterial assemblages present in Ornithodoros cf. hasei and its mammalian hosts, 84 ticks and 13 blood samples from bat hosts (Chiroptera) were selected, and the 16S rRNA gene V4 region was sequenced in five pools (each one related to each host-tick pairing). Bacterial taxonomic assignment analyses were performed by comparing operational taxonomic units (OTUs) shared between ticks and their host blood. This analysis showed the presence of Proteobacteria (38.8%), Enterobacteriaceae (25%), Firmicutes (12.3%), and Actinobacteria (10.9%) within blood samples, and Rickettsiaceae (39%), Firmicutes (25%), Actinobacteria (13.1%), and Proteobacteria (9%) within ticks. Species related to potentially pathogenic genera were detected in ticks, such as Borrelia sp., Bartonella tamiae, Ehrlichia sp. and Rickettsia-like endosymbiont, and the presence of these organisms was found in all analyzed bat species (Cynomops planirostris, Molossus pretiosus, Noctilio albiventris), and O. cf. hasei. About 41-48.6% of bacterial OTUs (genera and species) were shared between ticks and the blood of bat hosts. Targeted metagenomic screening techniques allowed the detection of tick-associated pathogens for O. cf. hasei and small mammals for the first time, enabling future research on many of these pathogens.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".