Bibliographic record
Abstract
This paper describes a disk-based algorithm for finding shortest paths in a large network system. It employs a strategy of processing the network piece by piece and is based on new algorithms for graph partitioning and for finding shortest paths that overcome the problem of existing approaches. To show that it is scalable to large network systems and is adaptable to different computing environments, seven states in Tiger/Line files are extracted as test cases and are experimented on machines with different configurations. The running time for finding the shortest path depends primarily on the power of the underlying systems. Moreover, to run the algorithm optimally, the memory requirement is not large, even for a very large network system such as the road system in several states in Tiger/Line file. To evaluate its performance, New Mexico state road system is used as the test case, and is compared with Dijkstra's algorithm. The average running time of the proposed algorithm is, in the worst case, about two and a half times slower than that of Dijkstra's algorithm; provided that in Dijkstra's algorithm, the whole graph can be fit into main memory and is already loaded in advance. If the I/O time for loading the whole graph is counted, the proposed algorithm is faster in essentially all cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".