Development of a Bio-Hybrid Mosquito Stinger-Based Atomic Force Microscopy Probe
Bibliographic record
Abstract
Mosquitoes, notorious as the deadliest animals to humans due to their capacity to transmit diseases, pose a persistent challenge to public health. The primary prevention strategy currently in use involves chemical repellents, which often prove ineffective as mosquitoes rapidly develop resistance. Consequently, the invention of new preventive methods is crucial. Such development hinges on a thorough understanding of mosquito biting behaviors, necessitating an experimental setup that accurately replicates actual biting scenarios with controllable testing parameters and quantitative measurements. To bridge this gap, a bio-hybrid atomic force microscopy (AFM) probe was engineered, featuring a biological stinger - specifically, a mosquito labrum - as its tip. This bio-hybrid probe, compatible with standard AFM systems, enables a near-authentic simulation of mosquito penetration behaviors. This method marks a step forward in the quantitative study of biting mechanisms, potentially leading to the creation of effective barriers against vector-borne diseases (VBDs) and opening new avenues in the fight against mosquito-transmitted illnesses.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".