Bibliographic record
Abstract
Blocked multiple-sequence alignment (BMA) refers to the construction of multiple alignments in DNA by first aligning conserved regions into what we call "blocks" and then aligning the regions between successive blocks to form a final alignment. Instead of starting from low-order pairwise alignments, we propose a new way to form blocks by searching for closely related regions in all input sequences, allowing internal spaces in blocks as well as some degree of mismatch. We address the problem of semi-conserved patterns (patterns that do not appear in all input sequences) by introducing into the process two similarity thresholds that are adjusted dynamically according to the input. A method to control the number of blocks is also presented to deal with the situation when input sequences have so many similar regions that it becomes impractical to form blocks by trying every combination. BMA is an implementation of this approach, and our experimental results indicate that this approach is efficient, particularly on large numbers of long sequences with well-conserved regions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".