Towards a practical trap for deer flies (Diptera: Tabanidae): initial tests of a bi-level Nzi trap
Bibliographic record
Abstract
Abstract A modified Nzi trap was tested at a residence and at a farm in eastern Ontario, Canada to better capture high-flying tabanids (Diptera) such as Chrysops Meigen. A new upper trap entrance was created to provide a higher and larger opening by reducing the front blue top shelf to half its height. To minimise escape of low-flying tabanids, a vertical inner baffle was added to direct low-flying tabanids up into the cone. Half of the tests of 18 new designs caught 1.5–2.7 times more deer flies than the Nzi trap did, with the other trap designs being as effective as the Nzi trap. The optimal design that maintained equal catches of other biting flies relative to the Nzi trap was one with a phthalogen inner horizontal shelf and a netting inner vertical baffle. This design is defined in the present as the “bi-level Nzi trap.” Chrysops entered the trap mostly through the top (88%; 17 spp.), along with Hybomitra Enderlein (94%; 12 spp.). Tabanus Linnaeus (9 spp.) entered through both entrances. The most abundant Tabanus, T. quinquevittatus Wiedemann, entered mostly through the bottom (70%), whereas Stomoxys calcitrans Linnaeus entered mostly through the top (92%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".