New materials for improving catches of horseflies (Diptera: Tabanidae) in Nzi traps
Bibliographic record
Abstract
New materials in 85 configurations were tested relative to standard fabric Nzi traps to improve catches of tabanids based on artificial visual cues that mimic those used in nature for locating hosts or water. Colour-fast synthetic fabrics and photo-selective horticultural mesh were tested to facilitate insecticide impregnation and for improved durability. Many plastics were explored to document how flies react to polarized and visible vs. ultraviolet light. Lastly, sticky horizontal, linearly polarizing ground-level shiny plastic targets were tested for capturing water-seeking tabanids relative to traps and vertical fabric targets. Based on the capture of 281 786 female Tabanidae (Tabanus Linnaeus, Hybomitra Enderlein, Chrysops Meigen), four alternative formats provided higher catches and/or durability. They were substituting: (1) phthalogen turquoise for phthalogen blue, (2) solution-dyed fabrics for blue and black cotton, (3) clear/white horticultural mesh for mosquito netting, and (4) clear, reflective plastic for some or all netting. A Sunbrella/BioNet fabric trap (portable) and a blue-black Plexiglas®/netting trap (fixed applications) consistently performed best (1.5-3 × higher catches). Ground-level targets captured many male and gravid female Hybomitra but were ineffective for Tabanus. Traps and vertical fabric or transparent sticky targets captured mainly unfed females.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".