Optimizing mountain railway alignments with a potential field guided 3D-RRT-star algorithm
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
Developing railway alignments becomes increasingly challenging in undulating terrain with densely-distributed obstacles. Traditional alignment optimization methods, which employ a design process characterized as “search for alignment-favorable environments based on trial alignments”, struggle to efficiently generate optimized alignments under these conditions. To address this problem, an environmental suitability analysis is first implemented by abstracting the study area as a set of voxels within various structural layers. The Environmental Suitability (ES) for each voxel is then formulated based on its location in different structural layers. Then, by considering the spatial distribution and the ES values, alignment-favorable regions and alignment-unfavorable regions are identified through a kernel density analysis and k-mean clustering. These regions are further represented by Potential Fields (PFields), which are integrated with a 3D Rapidly-exploring Random Tree-star (3D-RRT-star) to create a PField-RRT-star search method. Through application to a real-world mountain railway case, the PField-RRT-star method demonstrates improved search efficiency and solution quality.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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