Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given two sets of nodes P and Q on a road network, a k-Closest Pairs Query (k-CPQ) finds the pairs from P × Q which have the k smallest network distances. Although this problem has been well studied in the Euclidean and metric spaces, this is the first time it is being investigated in the more realistic case of road networks. As our first contribution, we present a new indexing structure, named G -tree, which is designed to support our proposed algorithms. Then, we propose, as our main contribution, two different approaches for processing k-CPQs. While the first approach applies a top-down traversal paradigm by applying a best-first search strategy, the second approach looks for the k-closest pairs by traversing the G*-tree in a bottom-up manner. Both of the these approaches employ an effective pruning strategy for shrinking the search space based on the minimum network distance between sub-graphs, which is main driver for the G*-tree's construction. Finally, we investigate the efficiency of the proposed approaches under a number of different parameters using real road networks.
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.
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 | 0.001 |
| 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