Alternate capacity reliability measures for transportation networks
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Bibliographic record
Abstract
SUMMARY Capacity reliability is defined as the probability that the network capacity can accommodate a certain volume of traffic demand at a required service level. It is a supply‐side reliability measure for assessing the adequacy of a degradable transportation network. The network capacity model used to calculate the capacity reliability measure is based on the concept of reserve capacity, which requires preserving a pre‐determined origin–destination (O–D) demand pattern. In this paper, we relax this assumption by allowing a non‐uniform growth in the spatial distribution of the O–D demand pattern. By using this non‐uniform O–D growth approach, two network capacity models related to the concepts of ultimate capacity and practical capacity are developed to estimate alternate capacity reliability measures. Numerical results are provided to analyze the features of three capacity reliability measures for transportation networks. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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