The effect of on-ramp and off-ramp on the one dimensional road with open boundaries
Bibliographic record
Abstract
The effect of one on-ramp (entry) and one off-ramp (exit) is investigated numerically in a one dimensional cellular automaton traffic flow model, with open boundary conditions, using parallel dynamics. Our aim in this paper is to study how the injection rates α and α0 and the extraction rates β and β0 act on the density and flux of cars in a one dimensional road. The priority of occupation at entry site i1 (removing at exit site i2) is attributed to the particle that entered (was absorbed) the chain. Phase diagrams in (β0, α0) and (β, α0) plans are established. For α = 0.1, they show three different topologies in the flow behaviour. The first one corresponds to the presence of four regions, by varying α0, namely low-density phase (LDP), intermediate-density phase (IDP), plateau current phase (PCP), and high-density phase (HDP). In the second topology, the intermediate-density phase disappears. The third topology presents only two regions, i.e., low- and high-density phases. For small values of α, the (IDP) and (PCP) disappear, respectively, by increasing β. When increasing α, the fourth topology, corresponding to one phase (HDP), appears for low values of β and β0.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".