GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper presents a new efficient solution to the Dynamic Shortest Path Routing Problem, using the principles of Generalized Pursuit Learning. It proposes an efficient algorithm for maintaining shortest path routing trees in networks that undergo stochastic updates in their structure. It involves finding the shortest path in a stochastic network, where there are continuous probabilistically based updates in link‐costs. In vast, rapidly changing telecommunications (wired or wireless) networks, where links go up and down continuously and rapidly, and where there are simultaneous random updates in link costs, the existing algorithms are inefficient. In such cases, shortest paths need to be computed within a very short time (often in the order of microseconds) by scanning and processing the minimal number of nodes and links. The proposed algorithm, referred to as the Generalized Pursuit Shortest Path Algorithm (GPSPA), will be very useful in this regard, because after convergence, it seems to be the best algorithm to‐date for this purpose. Indeed, it has the advantage that it can be used to find the shortest path within the ‘statistical’ average network, which converges irrespective of whether there are new changes in link‐costs or not. Existing algorithms are not characterized by such a behaviour inasmuch as they would recalculate the affected shortest paths after each link‐cost update. The algorithm has been rigorously evaluated experimentally, and it has been found to be a few orders of magnitude superior to the algorithms available in the literature. Copyright © 2004 John Wiley & Sons, Ltd.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.001 | 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