Alignment planning and network optimization of auxiliary roads for overhead power transmission line facility construction
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
To deploy overhead power transmission lines in a mountainous region, an auxiliary road (AR) network must be built to interconnect the site for pylons and the nearby existing roads, which poses challenges in safety, cost, environment, and efficiency. This research proposed a two-phase methodology: (1) auxiliary road alignment optimization (ARAO); (2) road network layout optimization (RNLO). ARAO devised the improved Dijkstra algorithm (IDA) to plan, under geometric design constraints, the AR alignments with minimal construction costs. RNLO utilized the genetic algorithm (GA) to screen out the AR network layout with minimal gross cost. The case studies substantiated the methodological superiority: compared with the human-planned design, the IDA-generated design curtailed excavation volume and total road length substantially. IDA planned the alignments with uplifted efficiency and shortened project duration. The IDA-plus-GA can optimize the network layout with minimal gross cost and robust adaptiveness to environmental constraints (e.g., forestry, waterbody, etc.).
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