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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".