Graphical Optimization Method for Symmetrical Bidirectional Corridor Progression
Bibliographic record
Abstract
The graphical progression method can obtain grand coordinated schemes with minimal computational complexity. However, there is no standardized solution for this method, and only a few related studies have been found thus far. Therefore, based on the in-depth discussion of the graphical optimization theory mechanism, a process-oriented and high-efficiency graphical method for symmetrical bidirectional corridor progression is proposed in this study. A two-round rotation transformation optimization process of the progression trajectory characteristic lines (PTC lines) is innovatively proposed. By establishing the updated judgment criteria for coordinated mode, the first round of PTC line rotation transformation realizes the optimization of coordinated modes and initial offsets. Giving the conditions for stopping rotation transformation and determining rotation points, rotation directions, and rotation angles, the second round of PTC line rotation transformation achieves the final optimization of the common signal cycle and offsets. The case study shows that the proposed graphical method can obtain the optimal progression effect through regular graphing and solving, although it can also be solved by highly efficient programming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".