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
A commonly used approach to self-location is for the robot to use point features or landmarks. Landmarks are typically difficult to detect and track with video or range sensors, and hence it is sensible to try to minimize the number of times the robot abandons the tracking of an already detected landmark to detect and pursue another. The problem addressed is how to select the landmarks that the robot is to detect and track over different parts of a given path. Several algorithms with different amounts of flexibility, generality and complexity are proposed. The authors address the uniform cost case (all landmarks have equal cost of detection and tracking), and the weighted cost case (each landmark has its own cost). The case of different sets of landmarks having different utility measures is also treated. The algorithm complexity is low-order polynomial in the number of landmarks k, the number of straight line segments of the path, and the number of shadows cast on the path by each landmark, except when taking into account the usefulness of landmarks in groups, which is exponential in k.
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