COMPUTING THE SET OF ALL THE DISTANT HORIZONS OF A TERRAIN
Bibliographic record
Abstract
We study the problem of computing the set of all distant horizons of a terrain, represented as either: (1) the set of all edges that appear on the distant horizon from at least one viewing direction; (2) for every edge e, the set of direction intervals for which e appears on the distant horizon; or (3) a search structure to query for the edges on the distant horizon, or the precise distant horizon, from a fixed viewing direction. We describe an algorithm that solves the first and second forms of the problem in O(n2+∊) time for any constant ∊ > 0 where n is the number of edges of the terrain. This algorithm can be extended to compute a search structure for (3) in O(n2+∊) time. The search structure can return the s edges on the distant horizon in O( log n + s) time. We show solving problem (1) is 3SUM hard. Furthermore, we construct a terrain with a single local maximum in which Θ(n) edges each have Θ(n) direction intervals, showing that our solution to (2) cannot be significantly improved, in the worst case, even for such restricted terrains. This takes advantage of a novel construction in which the convex hull of a set of n linearly moving points, whose trajectories do not intersect, changes Ω(n2) times.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".