Bibliographic record
Abstract
On January 9th, 2021, the Laboratory Medicine & Pathobiology Students Union (LMPSU) hosted their annual scientific conference online, focusing on the theme: “COVID-19: A viral phenomena”. The department of Laboratory Medicine & Pathobiology (LMP) at the Temerty Faculty of Medicine, University of Toronto is home to world-class research in the area of pathobiology, from cancer to immunopathology to neuropathology. The conference began with opening remarks from LMPSU executives Karen Mao and Ziqi Liu, followed by Dr. Rita Kandel, the chair of the department of LMP. The topic of COVID-19 research was timely, to say the least! Invited speakers were asked to share their research and knowledge about various aspects of the COVID-19 pandemic from basic virology to treatment options, and epidemiology. The keynote speakers were Dr. Samira Mubareka and Dr. Robert Kozak; notably members of the team that was among the first to isolate the SARS-CoV-2 virus.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".