Spatial Repartition of Tabanids (Diptera: Tabanidae) in Different Ecological Zones of North Cameroon
Bibliographic record
Abstract
The different species of tabanids and their zoogeographical distribution in the North Region of Cameroon needs to be updated following the preliminary study of Ovazza and collaborators in 1970.To achieve this, an inventory of tabanids in three key ecological zones of North Cameroon was implemented with a modified Manitoba trap (Sevi), Nzi, Vavoua and Laveissière traps (n=39); identification was made using standard keys.The total number of tabanids captured was 723, belonging to the following species: Tabanus gratus (42.32%),Chrysops distinctipennis (19.50%), T. taeniola (13.83%), T. biguttatus (8.99%), T. sufis (8.29%) and T.par (7.05%).The six species identified were common in all the three ecozones.The species in the different genera identified showed significant (P˃0.05)differences in body and wing length.Tabanids abundance was biotope-dependent that is-Zone 26; 282/723 (39.0%),Zone 27; 230/723 (31.8%) and Mbele 211/723 (29.2%).Based on the diversity index of the various tabanid species, Mbele was most diversified in terms of species-type, followed by Zone 27 and lastly by Zone 26. C. distinctipennis and T. sufis were identified in the Northern region, for the first time.This has added to the already existing list of tabanids of Cameroon and will be a necessary element for future control in the North Region of this country.
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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".