Annotating the Mycobacterium avium Genome: A Project in Bioinformatics
Bibliographic record
Abstract
Bioinformatic tools facilitate efficient processing and formatting of experimental data and are becoming essential to research in the biological sciences. Whole genome sequencing projects, combined with DNA microarray technology, have allowed genomic comparisons between and within species of microorganisms. The genome of Mycobacterium avium subsp. avium (MAA) has been sequenced by The Institute for Genomic Research (TIGR), but a final and annotated version has not yet been made available. The goal of this project was to annotate the sequence of MAA as a foundation for microarray-based genomic comparisons. We used software to identify and predict open reading frames (ORFs) present in this organism. The ORFs were then compared to those catalogued in two large, online genetic databases for other microorganisms and matched to homologous sequences, allowing the determination of putative functions for each predicted gene. The genome of MAA was determined to contain 4480 genes, the majority of which are homologous to genes found in other Mycobacterial species.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".