Bibliographic record
Abstract
Navigation is a skill used by all life. Although many plants and animals have developed natural navigation methods, humans typically employ a compass. A magnetic compass relies on the Earth's magnetic field. In the presence of other magnetic fields, such as in many electrical systems, its efficacy is degraded. For this reason, a new form of compass not reliant on the orientation of a magnetic field is investigated. The proposed method uses the atmospheric polarization of sunlight to determine heading. As light rays hit the atmosphere, they are scattered by gas molecules. During a scattering event, the E-vector of a ray is uniformly oriented with respect to the sun position, as described by the Rayleigh model. E-vector angles (AoLP) are consistently orthogonal to the solar meridian. This feature of the Rayleigh model can be used to determine the solar meridian from a polarized sky image. Sky images were taken using a polarizing camera and fed to a computer for classification. The classifier was based on a method published by Lu et al. It has three steps: Extract the AoLP from intensity data. Generate a binary image by selecting pixels representing an orthogonal AoLP. Apply a Hough transform to the binary image to extract heading. During testing, the compass was aligned with north. Then, an image was taken and classified. The solar azimuth angle in the camera reference frame was read as 125°. The recognized solar meridian with respect to north was 131°. Since the camera was aligned with north, these angles are comparable. The compass error was estimated at ±6°. The help of my colleagues has been integral, and I’d like to thank them. Truly, the work done on this project was made possible by ongoing guidance from Dr. Muhammad Alam. I am grateful for his support.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".