Sensing and sensing-of-sensing with drones
Bibliographic record
Abstract
Vehicles such as cars, mobility scooters, wheelchairs, and the like are becoming partially or fully automated (i.e. equipped with driver-assist technologies or completely self-driving technologies). These technologies rely heavily on sensors such as vision (cameras), radar, sonar, etc.. In this paper, we propose the use of autonomous craft (e.g. “drones”) for scientific meta-measurement, i.e. specifically sensing-of-sensors and sensing their capacity to sense (metasensing). In particular, we show how a drone can be used to characterize a sonar sensing device. Sonar sensing devices are often used on autonomous vehicles. Our two main contributions are: (1) the use of drones for the sensing of acoustic sensors (e.g. sonar transducers), and (2) minimizing the metasensing flight paths by following phase contours (i.e. vector scanning rather than raster scanning), so that a small number of flights can provide meaningful insight into the characteristics of an acoustic sensor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".