Bibliographic record
Abstract
Rover navigation currently relies on algorithms to automatically determine their path. This is because the distance from Earth to Mars means that real time communication is impossible. Current navigation algorithms have difficulties in identifying terrain, causing problems such as becoming stuck in soft terrain. Furthermore, the available computation power and memory are limited on a rover. This paper presents both a modified U-Net model to identify parts of the terrain and combining multiple classes to have less output classes. The proposed method was to combine classes like soil and bedrock into more generalized classes, like traversable and untraversable, to reduce memory usage and needed computational power. Combining the classes shows that a model can be trained faster, and in some cases even improve. Testing this method on low resolution images has shown improved results in testing. After training, a three-class model is able to yield a higher mIoU of 0.4583 on a test set compared to the full five-class model, which achieved 0.3451. This method is non-specific to U-Net and can be applied to many different models. Combining this method with other models and larger datasets during training could be an option of improving the accuracy of models running on less processing power, allowing for use on platforms such as Mars rovers.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".