Bibliographic record
Abstract
We investigate the problem of how to evaluate, fast and efficiently, classes of optimal route queries on a massive graph in a unified framework. To evaluate a route query effectively, a large network is partitioned into a collection of fragments, and distances of some optimal routes in the network are pre-computed. Under such a setting, we find a unified algorithm that can evaluate classes of optimal route queries. The classes that can be processed efficiently are called constraint preserving (CP) which include, among others, shortest path, forbidden edges, forbidden nodes and α-autonomy optimal route query classes. We prove the correctness of the unified algorithm. We then turn our attention to the optimization of the proposed algorithm. Several pruning and optimization techniques are derived that minimize the search time and I/O accesses. We show empirically that these techniques are effective. The proposed optimal route query evaluation algorithm, with all these techniques incorporated, is compared with a main-memory and a disk-based brute-force CP algorithms. We show experimentally that the proposed unified algorithm outperforms the brute-force algorithms, both in term of CPU time and I/O cost, by a wide margin.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".