The provision of bioinformatics services in Canadian academic libraries
Bibliographic record
Abstract
Introduction – This article describes the level of bioinformatics services offered by academic libraries across Canada. It also assesses faculty use of bioinformatics resources and the need for library bioinformatics services at one academic institution, Concordia University. Methods – To assess the level of bioinformatics services at Canadian universities, a survey was sent to life and health sciences librarians at English-speaking Canadian universities comparable to Concordia University. To assess faculty use of bioinformatics and the need for bioinformatics instruction, another survey was sent to faculty of the Centre for Structural and Functional Genomics at Concordia University. Results – Approximately one-quarter of librarians surveyed provided services such as online research guides for bioinformatics resources, workshops, or online tutorials. Individual consultations with students were infrequent. The majority of the libraries where bioinformatics services were offered were at universities with a medical school. The faculty survey indicated that Concordia Centre for Structural and Functional Genomics researchers are heavy users of bibliographic and bioinformatics databases, using at least one of these databases on a daily basis. Most faculty members learned how to use bioinformatics databases on their own and regularly teach the use of these databases to their students or colleagues. Nevertheless, faculty at Concordia seem to be open to some form of collaboration with the library for the provision of bioinformatics services. Discussion – Although librarians can participate in the teaching of bioinformatics database skills, library services in bioinformatics at Canadian university libraries are still in the embryonic phase. Librarians should be trained in the use of these databases to increase their confidence and expertise and to help them market these skills to faculty and students.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".