Composite Rectilinear Deformation for Stretch and Squish Navigation
Bibliographic record
Abstract
We present the first scalable algorithm that supports the composition of successive rectilinear deformations. Earlier systems that provided stretch and squish navigation could only handle small datasets. More recent work featuring rubber sheet navigation for large datasets has focused on rendering and on application-specific issues. However, no algorithm has yet been presented for carrying out such navigation methods; our paper addresses this problem. For maximum flexibility with large datasets, a stretch and squish navigation algorithm should allow for millions of potentially deformable regions. However, typical usage only changes the extents of a small subset k of these n regions at a time. The challenge is to avoid computations that are linear in n, because a single deformation can affect the absolute screen-space location of every deformable region. We provide an O(klogn) algorithm that supports any application that can lay out a dataset on a generic grid, and show an implementation that allows navigation of trees and gene sequences with millions of items in sub-millisecond time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".