Modelling the thermal effects of stimulus airflow from the Dolphin aesthesiometer on a model eye surface
Bibliographic record
Abstract
Abstract Purpose To assess the ability of the Dolphin air‐pulse aesthesiometer to present multiple stimuli, which are separated temporally (in sequence) or spatially (simultaneously). Methods Two studies were performed to explore the cooling effects induced by double air‐puff stimuli generated by a novel aesthesiometer composed of two micro‐blower integrated units. The stimuli were delivered sequentially or simultaneously at the same or different spatial locations to an in vitro eye model monitored using thermography. The model eye was based on a 2‐cm LED dome light mounted on a circuit board with an 8‐V supply producing a baseline 32°C temperature. Single and repeated air‐pulse stimuli varying in intensity, duration, inter‐stimulus delay and stimulus location were presented to the model. The cooling effect produced was observed using a thermal camera and quantified using image analysis software. Results The instrument can deliver single stimuli, repeated single stimuli with a variable time delay or multiple stimuli either simultaneously or with a time delay between them. The thermal effects of stimuli were evaluated by measuring (relative to pre‐stimulus baseline) the local temperature change and the diameter of the model eye surface region with ≥1°C reduction. Repeated stimuli at the same location produced a significantly greater effect than a single stimulus of the same intensity (larger area of cooling after the second stimulus compared to the first [| M |{SE} = 1.48 mm {0.06}, p < 0.001]). Spatially separated stimuli produced separate cooling zones, with the amount of cooling relative to stimulus intensity (rm‐ANOVA, F 2,8 = 276.01, p < 0.001, 2 = 0.96). Conclusions The combined use of two micro‐blower units allows increased options for modification of stimulus intensity and timing of delivery that enables the production of alternative stimulus presentations and intensity compared to a single stimulus. This adaptability may enable future in vivo study of corneal sensory nerve summation.
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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".