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
Presents the application of an image registration method for a mobile manipulator. The robot is used for scanning natural terrain and detecting metal objects hidden beneath the terrain surface (e.g., landmines) using a metal detector. The range image may be interpreted for visual servoing, map building and path planning, or object recognition. In the work, the image is used to build a terrain map for obstacle free path planning. Because the working area of the robot is extremely dynamic (i.e., not only the robot travels but also the environment is also subject to change) an active range sensing method is selected to provide the range image. The range values are acquired using a laser range finder with a rotating mirror for scanning so that sensor fusion in the form of collecting sensor readings over an extended period of time is required. In addition, range readings of two ultrasonic range finders are fused at signal level to tackle both sensor imperfection and environmental illumination that induce uncertainty at the system. We explain the use of a real-time programming platform that executes an online map-building process in parallel for robot manipulation and control.
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.
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 it