State-of-the-art Report of Research about Multi Sensor Image-based Navigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This report aims to describe the latest research and method developmentof image-based multi sensor fusion navigation and summarizes open aerialdatasets which can support the latest research related to this project. Itsupports the initial setting of the direction of the algorithm development inthe early stage of the project.The Multi Sensor Image-based Navigation project aims to study and developthe methods focusing on image-based multisensor navigation in orderto acquire a precise localization of the aircraft. GNSS-based localizationand navigation systems are sensitive to disturbances and jamming, hencethe capability to provide reliable position accuracy without GNSS is a keyelement to develop the navigation systems.The output of this project can be utilized in a wide range of applications,such as aircraft operation in GNSS denied environments or urban air mobilitycontext.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it