Best-Compromise In-Route Nearest Neighbor Queries
Bibliographic record
Abstract
Humans are animals of habit, e.g., people follow typical and/or familiar paths in their daily routines. With that in mind we investigate the problem where a user, traveling on his/her preferred path, needs to visit one of many available points-of-interest while (1) minimizing his/her total travel distance and also (2) minimizing the detour distance incurred to reach the chosen point-of-interest. We call this new problem the "Best-Compromise In-Route Nearest Neighbor" query in order to emphasize that a route cannot typically optimize both criteria at the same time, but rather find a compromise between them. In fact, the competing nature of these two criteria resembles the notion of skyline queries. In that context, we propose a solution based on using suitable upper-bounds to both cost criteria to prune uninteresting paths. It returns all linearly non-dominated paths that are optimal under any given linear combination of the two competing criteria. Our experiments using real data sets of different sizes show that our proposal can be orders of magnitude faster than a straightforward alternative.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".