Bibliographic record
Abstract
Shortest path problems are a well-studied class of problems in theoretical computer science. One particularly applicable type of shortest path problem is to find the geodesic shortest path on a terrain. This type of algorithm finds the shortest path between two points that stays on the surface of a terrain. The most popular methods for finding such a shortest path involve a variant of Dijkstra’s algorithm and run in time approximately !$#&% in the size of the terrain [5, 4]. These algorithms for calculating shortest paths on a terrain require a precise input; any errors in measuring the terrain translate into errors in the output of the algorithms. What appears to be a shortest path according to the given input may turn out to be longer than an alternate path in reality. Uncertain terrains are a new model for acknowledging and dealing with these errors. In this paper, we consider one version of the shortest path problem on uncertain terrains: the optimistic shortest path. Essentially, we would like to find the path whose length is smallest over all paths and over all possible real terrains. This seems to be a slight generalization of the traditional geodesic shortest path problem. We show that it is, in fact, more akin to the problem of finding the shortest path in three dimensions that avoids polyhedral obstacles. This problem was shown to be NP-hard by Canny and Reif [3] in 1986. It is from their proof that our work is derived.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".