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
Terrain assessment and path planning are intrinsically linked. There exist a variety of terrain-assessment algorithms and these methods follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. We present a modular path-planning algorithm that uses a hierarchy of terrain-assessment methods; from low-fidelity to high-fidelity. Using all the available sensor data, the visible terrain is assessed with the low-fidelity, low-cost method. The decision to assess a piece of terrain with the high-fidelity, high-cost method is made considering potential path benefits and the cost of assessment. The result is a lower combined cost of the path and terrain assessment that exploits the capabilities of the robot chassis where prudent. We demonstrate the technique on a large number of simulated path-planning problems using fractal terrain, as well as provide preliminary results from an experimental field test carried out on Devon Island, Canada.
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 | 0.001 | 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 | 0.001 | 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