Bibliographic record
Abstract
A novel solution is presented to the Nearest Neighbor Problem that is specifically tailored for determining correspondences within the Iterative Closest Point Algorithm. The reference point set P is preprocessed by calculating for each point p/spl I.oarr//sub i//spl isin/P that neighborhood of points which lie within a certain distance /spl epsiv/ of p/spl I.oarr//sub i/. The points within each /spl epsiv/-neighborhood are sorted by increasing distance to their respective p/spl I.oarr//sub i/. At runtime, the correspondences are tracked across iterations, and the previous correspondence is used as an estimate of the current correspondence. If the estimate satifies a constraint, called the Spherical Constraint, then the nearest neighbor falls within the /spl epsiv/-neighborhood of the estimate. A novel theorem, the Ordering Theorem, is presented which allows the Triangle Inequality to efficiently prune points from the sorted /spl epsiv/-neighborhood from further consideration. The method has been implemented and is demonstrated to be more efficient than both the k-d tree and Elias methods. After /spl sim/40 iterations, fewer than 2 distance calculations were required on average per correspondence, which is close to the theoretical minimum of 1. Furthermore, after 20 iterations the time expense per iteration was demonstrated to be negligibly more than simply looping through the points.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".