A New Method for Wide Frequency Range Dynamic Olfactory Stimulation and Characterization
Bibliographic record
Abstract
Sensory receptors often receive strongly dynamic, or time varying, inputs in their natural environments. Characterizing their dynamic properties requires control and measurement of the stimulus over a frequency range that equals or exceeds the receptor response. Techniques for dynamic stimulation of olfactory receptors have lagged behind other major sensory modalities because of difficulties in controlling and measuring the concentration of odorants at the receptor. We present a new method for delivering olfactory stimulation that gives linear, low-noise, wide frequency range control of odorant concentration. A servo-controlled moving bead of silicone elastomer occludes the tip of a Pasteur pipette that releases odorant plus tracer gas into a flow tube. Tracer gas serves as a surrogate indicator of odorant concentration and is measured by a photoionization detector. The system has well-defined time-dependent behavior (frequency response and impulse response functions) and gives predictable control of odorant over a significant volume surrounding the animal. The frequency range of the system is about 0-100 Hz. System characterization was based on random (white noise) stimulation, which allows more rapid and accurate estimation of dynamic behavior than deterministic signals such as sinusoids or step functions. Frequency response functions of Drosophila electroantennograms stimulated by fruit odors were used to demonstrate a typical application of the system.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".