Bibliographic record
Abstract
In this paper we present a slightly modified definition of snap rounding, and provide two ecient algorithms that perform this rounding. The first algorithm takes n line segments as input and generates the set of snapped segments in O(|I| + c is(c)logn + |I m|), where |I| is the complexity of the unrounded arrangement I, is(c) is the number of segments that have an intersection or endpoint in pixel column c, and I m is the multi- set of snapped segment fragments. The second algo- rithm generates the rounded arrangement of segments in O(|I| + c is(c)logn + |I |logn), where |I | is the complexity of the rounded arrangement I. Both use simple integer arithmetic to compute the rounded ar- rangement by sweeping a strip of unit width through the arrangement, are robust, and are practical to im- plement. They improve upon existing algorithms, since existing running times either include a logarithmic fac- tor in |I|, (i.e., |I| logn), or depend upon the number of segments interacting within a particular hot pixel (is(h) and ed(h) (7), or |h| (3)), whereas ours are linear in |I| and depend upon the number of segments interacting in an entire hot column (is(c)), which is a much coarser partition of the plane.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".