Bibliographic record
Abstract
Many emerging technologies like autonomous driving, robotic agriculture, and drone control promise to improve safety and efficiency in multiple sectors, however they require high-precision navigation systems which are currently insufficient. Existing navigation approaches rely on the global navigational satellite system (GNSS) that is prone to failure when satellite connection is unavailable. GNSS signal loss occurs in dense urban areas, which makes vehicle localization a challenge. Perhaps more problematically, jamming devices that block GNSS signals are becoming increasingly accessible. These inherent problems have pushed researchers to investigate methods of augmenting GNSS navigation with additional sensors to improve its precision and robustness. One such augmentation is inspired by the biology of the desert ant. This ant uses adaptations in its eyes to extract navigational cues from polarized skylight. When light from the sun hits the atmosphere, it generates a uniform polarization pattern that can be used to develop and maintain a navigational plan. Our work begins by constructing a polarization compass (PC) from the biological insights found in the desert ant. We then consider three algorithms for extracting navigational cues from the skylight polarization pattern captured by the PC: Hough transform (HT), support vector machine (SVM), and linear regression (LR). We demonstrate that PC provides a heading estimation that is accurate to less than 1 degree, which is better than existing magnetic compasses. Our work shows that PC is an attractive augmentation for GNSS-based systems which may improve their performance under adverse conditions. In future work, the PC will be integrated with other sensors, including GNSS, to assess its performance under realistic conditions. This will be carried out in both terrestrial and aerial domains.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".