Odd Distance Anchors for Rapid Clustering
Bibliographic record
Abstract
This paper introduces a rapid clustering algorithm called anchor clustering. The algorithm was invented to permit clustering of lymphocyte antigen receptor sequences for veterinary diagnostic applications. Anchor clustering has a slow off-line and a rapid on-line phase. The off-line portion consists of locating a set of points, called anchors, by packing points into the data space so that they satisfy a minimum distance constraint. This study addresses a problem in the anchor location phase of anchor clustering. When used on discrete data, like DNA under the Hamming metric, there is a problem with data that exhibit tied minimum distances to anchors. This can be addressed by finding sets of anchors in which as many of the small distances between anchors are odd. This study tests four methods for locating sets of anchors enriched for short, odd distances. One method, which explicitly scores anchor sets on avoiding ties, works very poorly. A method that encourages odd distances is somewhat more effective, but two methods that optimize the ratio of short odd distances to short even distances achieve substantial enrichment of short odd distances. The reasons for the observed performance of different techniques are explored and possible next steps are outlined.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".