Multimodal floral cues guide mosquitoes to tansy inflorescences
Bibliographic record
Abstract
Abstract Female mosquitoes exploit olfactory, CO2, visual, and thermal cues to locate vertebrate hosts. Male and female mosquitoes also consume floral nectar that provides essential energy for flight and survival. Heretofore, nectar-foraging mosquitoes were thought to be guided solely by floral odorants. Using common tansies,Tanacetum vulgareL., northern house mosquitoes,Culex pipiensL., and yellow fever mosquitoes,Aedes aegypti(L.), we tested the hypothesis that the entire inflorescence Gestalt of olfactory, CO2and visual cues is more attractive to mosquitoes than floral odorants alone. In laboratory experiments, we demonstrated that visual and olfactory inflorescence cues in combination attract more mosquitoes than olfactory cues alone. We established that tansies become net producers of CO2after sunset, and that CO2enhances the attractiveness of a floral blend comprising 20 synthetic odorants of tansy inflorescences. This blend included nine odorants found in human headspace. The “human-odorant-blend” attracted mosquitoes but was less effective than the entire 20-odorant floral blend. Our data support the hypothesis that the entire inflorescence Gestalt of olfactory, CO2and visual cues is more attractive to mosquitoes than floral odorants alone. Overlapping cues between plants and vertebrates support the previously postulated concept that haematophagy of mosquitoes may have arisen from phytophagy.
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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 | 0.000 | 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) | 0.001 | 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".