Proceedings of the 14th annual RiboWest conference: perspectives and outcome
Bibliographic record
Abstract
The RiboWest Conference brings together RNA researchers in Canada with the 2-fold goals of fostering internationally competitive RNA research and of training the next generation of scientists. The 14th Annual RiboWest conference (RiboWest 2018) was held at the University of Lethbridge (Lethbridge, Alberta) from June 10th to 13th, 2018. This meeting was focused on all major aspects of RNA research, ranging from understanding the cellular role of RNA, studying RNA interactions and structures, and employing them as a therapeutic tool. The invited keynote speakers (5) provided insights into the wide-range of RNA-based research. One of the unique features of this conference was that the majority of the oral presentations were given by the trainees (undergraduate/graduate students and postdoctoral researchers). Hosted by the Alberta RNA Research and Training Institute (ARRTI) at the University of Lethbridge as the leading center of RNA research in Western Canada, the RiboWest 2018 was well attended by researchers from across the country (>110 attendees in total). This conference proceedings editorial presents the overview of the conference, and briefly introduces articles published in this special issue of Biochemistry and Cell Biology.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".