Introducing mass balancing theorem for network flow maximization
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Bibliographic record
Abstract
Maximization of flow through the network is required in many practical applications such as water supply flow networks, Oil and Gas flow networks, and transportation networks etc. In this paper a new theorem is presented that has direct application on maximization of flow through the network. This theorem suggests that the maximization of network flow can be achieved by visiting only unbalanced nodes rather than the whole network. Therefore based on this theorem a method is developed that maximizes flow thorough the network by visiting only unbalanced nodes. Hence this method can achieve solution in a sub-linear time where network has fewer unbalanced nodes. However this method has worst case complexity of order O(m2-m), where m is the number of edges. Furthermore it is shown that this theorem has also potential to make optimization an easier task in a multi-commodity flow environment.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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