Todos Santos small RNA symposium
Bibliographic record
Abstract
Worm biologists from the United States, Canada, and the United Kingdom gathered at the Colorado State University Todos Santos Center in Baja California Sur, Mexico, April 3–5, 2019 for the Todos Santos Small RNA Symposium. Meeting participants, many of whom were still recovering from the bomb cyclone that struck a large swath of North America just days earlier, were greeted by the warmth and sunshine that is nearly ubiquitous in the sleepy seaside town of Todos Santos. With only 24 speakers, the meeting had the sort of laid-back vibe you might expect amongst the palm trees and ocean breeze of the Pacific coast of Mexico. The meeting started with tracing the laboratory lineages of participants. Not surprisingly, the most common parental lineages represented at the meeting were Dr. Craig Mello, Dr. Gary Ruvkun, and Dr. Victor Ambros, whom, together with Dr. Andy Fire and Dr. David Baulcombe, pioneered the small RNA field. In sad irony, on the closing day of the meeting, participants were met with the news of Dr. Sydney Brenner’s passing. By establishing the worm, Caenorhabditis elegans, as a model system Dr. Brenner paved the way for much of the research discussed 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.022 |
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".