Letting the Genes Out of the Bottle: Making the Most of Free Bioinformatics Information
Bibliographic record
Abstract
Academic libraries can add quality to their services by going beyond standard bibliographic resources. In order to maintain relevance to the teaching and research missions of our institutions, libraries need to widen their range of information sources to include emerging data repositories. Bioinformatics provides a clear example of a discipline rich in freely available information that students and faculty can use to create new knowledge. Adding instruction in using key genetics databases to an existing suite of information literacy sessions allowed this librarian to expand his own subject knowledge, develop a productive collaboration with a faculty member and provide a high-quality, innovative learning experience for students. This session will place bioinformatics databases such as OMIM (Online Mendelian Inheritance in Man), BLAST (Basic Local Alignment Search Tool), and ExPASy’s ProtParam (Protein Parameter) within the context of library reference and instruction. Critical aspects of each resource and the joys and pitfalls of learning and using them will be outlined as examples for effectively incorporating non-bibliographic resources into research assignments. Participants will be invited to contribute their own experiences with using similar tools.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".