Bibliographic record
Abstract
Although many commercial packages exist for molecular sequence analysis, they are typically expensive. Whereas many Web-based applications are available for sequence analysis, the Web interface cannot store data on remote servers and is awkward to use. A good alternative is to build a sequence analysis facility on a local server. BIRCH, the Biological Research Computer Hierarchy, is an example of such a system ( http://home.cc.umanitoba.ca/~psgendb and ref. 1). BIRCH is best thought of as a workbench containing tools for working with sequences, as well as software that minimizes the problems of putting tools together to perform a task. For example, in Fig. 1 , several steps in phylogeny construction from an alignment were performed automatically. It is not possible to provide detailed instructions on installing all of the 300+programs that currently reside in BIRCH. Rather, my purpose is to outline the strategies and tricks that make building and maintaining a sequence facility a smooth ongoing task. Automated phylogeny. In GDE ( 2 ), aligned sequences were selected, and fastDNAml ( 3 ) was called to produce a phylogeny. GDE automatically calls a text editor and treetool ( 4 ) to display results. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.117 | 0.095 |
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".