Bibliographic record
Abstract
Abstract BLAST (Basic Local Alignment Search Theorem) is one of the most elegant, and widely used bioinformatics analysis developed to date. Like many bioinformatic analyses, BLAST uses evolutionary theory as the basis for its assumptions. Therefore, an understanding of evolutionary theory is critical to the appropriate interpretation, and further analysis, of BLAST results. Viewing essentially one‐dimensional BLAST analysis from the perspective of a two‐dimensional phylogenetic analysis has a number of benefits including more accurate identification of the true “top hit”, delineation of gene families, identification of true homologs, and improved functional assignment of orthologs and paralogs. Performing such phylogenetic analyses has become easier, now that semiautomated methods have been developed that permit rough phylogenetic overviews of the data. However, it should be emphasized that phylogenetic analysis must often be customized for a given experiment or research question. Such issues are relevant not only to the further analysis of a BLAST output, but also to similar analyses of outputs from other widely used algorithms for rapid database search. Critical analysis of results is becoming increasingly important as sequence databases increase in both size and complexity – and as we begin to understand that even these large sequence databases are only scratching the surface of the true complexity and variety of sequences that exist in nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".