Science AMA Series: This is Chris Deeg of the University of British Columbia (Canada). I do research on Giant Viruses that infect microscopic organisms and I’m here today to talk about it. AMA!
Bibliographic record
Abstract
hi reddit! I’m a graduate student in Curtis Suttle’s lab at the University of British Columbia (Canada) where our research focuses on aquatic microbiology. I study pathogens that infect protists – microscopic organisms living in aquatic environments. Amongst them are Giant Viruses that have challenged concepts of what constitutes a virus due to their enormous size and complexity. My research aims to explore the diversity and environmental role of these overlooked viruses. Further, I am interested in the evolutionary processes that have led to Giant Viruses reaching a complexity comparable to cellular organisms. In a recent paper published in the journal eLife, my colleagues and I isolated and characterized the giant Bodo saltans virus (BsV) that infects the protist Bodo saltans. Sequencing the genome of BsV revealed many previously unknown genes, a putative mechanism for genome expansion, and several unusual features, such as movable genetic elements that might help to fend off other Giant Viruses by cutting their genomes. You can read a plain-language summary of our findings. I’m here to answer questions related to our eLife paper or our research more broadly. I’ll start answering questions at 1pm EDT. AMA!
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.136 | 0.170 |
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".