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Synergism between ammonia and phenols for <i>Hybomitra</i> tabanids in northern and temperate Canada

2011· article· en· W2083008327 on OpenAlexaffabout
Steve Mihok, K. Lange

Bibliographic record

VenueMedical and Veterinary Entomology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsNuisanceTemperate climateBiologyPhenolsEcologyAmmoniaAnimal scienceBiochemistry

Abstract

fetched live from OpenAlex

Baits for tabanids (Diptera: Tabanidae) were tested in the Northwest Territories (60 °N) and Ontario (45 °N) using Nzi traps. Tests targeted ammonia, phenols/cow urine and octenol. About 200 000 tabanids were captured in 15 experiments with a maximum capture of 4182 in one trap in 1 day. In the Northwest Territories, phenols, urine and octenol were effective single baits for only some species. At both locations, adding ammonia to an unbaited or an octenol-baited trap had no effect on catches. By contrast, catches were increased for several species when ammonia was combined with phenols or urine. In Ontario, including ammonia in various baits increased catches by 1.5- to 3.4-fold relative to octenol alone for three Hybomitra and one Tabanus species. Synergism between ammonia and phenols was clearly demonstrated for the dominant Hybomitra species in Ontario (Hybomitra lasiophthalma), but not for the dominant species in the Northwest Territories (Hybomitra epistates). In five other northern Hybomitra species, baits of ammonia and/or octenol in combination with phenols resulted in a 1.7- to 4.1-fold increase in catch relative to an unbaited trap. Further tests of ammonia as a synergist for biting flies may prove useful in, for example, tsetse, which respond strongly to phenols.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.234
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2011
Admission routes2
Has abstractyes

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