GPCR Retreat 2014: a good view leads to many discoveries!
Bibliographic record
Abstract
The GPCR gods smiled on us last year as the 15th Annual GPCR Retreat was held last October 2nd-4th in Bromont, Québec. The fall colors were at their peak and the meeting attendees were also in fine form. The program was one of the best we have seen at any GPCR-related meeting in years and there was a great deal of excitement about new methodological approaches to understanding receptor biology, new concepts in GPCR signaling and a continued emphasis on translation of these discoveries. This year was also the first year we opened the meeting with a short course on biased agonism and how to measure and analyze it.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.081 | 0.049 |
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".