Bibliographic record
Abstract
In London, Ontario, the 13th Annual Joint meeting of the Great Lakes GPCR Retreat and the Club des Récepteurs à Sept Domaines Transmembranaires (known simply as the GPCR Retreat) was held on 17-19 October 2012, organized by Steve Ferguson and Peter Chidiac. This meeting gathered together a core group of investigators from Michigan, Ontario and Québec and has steadily increased its attendance in both the eastern (Europe) and western (USA, Canada) directions. This year's buzz naturally centered around the Nobel Prize in Chemistry, which was won the week before by Brian Kobilka and Robert Lefkowitz for their work on receptor structure and function. Michel Bouvier provided a heartfelt tribute to one of the attendees, Marc Caron, a pioneer in the GPCR field, has made many contributions to the work that led to this year's Nobel Prize. The meeting featured interesting sessions on the physiological roles of GPCRs in the nervous system, circadian biology and cancer, dealing at the cellular and molecular level with GPCR, G protein and effector structure and function, regulation and trafficking--with an overall focus on how to move molecular pharmacology in vivo.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.028 |
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".