Letter from the Editors: The NSERC-CREATE Edition
Bibliographic record
Abstract
It is with a great sense of privilege that we publish the abstracts from this year’s NSERC-CREATE Symposium at the University of Calgary. Every year the CREATE Symposium provides undergraduate students in biomedical-related disciplines with the opportunity to share the groundbreaking research that they have performed over the summer. CREATE, which stands for “Collaborative Research and Training Experience Program” truly lives up to its name – these students have not only performed original research, but have also learnt valuable lessons about the world of academia, academic publishing, and research presentation. This year, the CREATE program asked JURA if we would be interested in publishing the abstracts for the podium presentations at the symposium. Of course we responded enthusiastically to this proposition and the result is what you see in front of you: twenty-two abstracts representing the some of the best undergraduate research undertaken at the University of Calgary in 2011. We are extremely proud to publish all of them. The second volume of JURA is currently under development for research performed in 2011. We encourage anyone to submit a letter, a general-interest article, a review article, or an original research article by January 1, 2012 for publication. Please visit http://www.ucalgary.ca/jura for more details, and in the meantime, enjoy the hard work of our research undergraduates presented here!
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.029 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".