Keystone Symposium on Antibodies as Drugs
Bibliographic record
Abstract
The symposium on Antibodies as Drugs, organized by Keystone Symposia and chaired by J. Marks, (University of California Los Angeles, USA), E.S. Ward (University of Texas Southwestern Medical Center, USA) and L. Weiner (Georgetown University Medical Center, USA), was held in Whistler, British Columbia. This Canadian Rockies village, which will host the 2010 Olympic Games, served as an enchanting backdrop to the meeting. The more than 350 speakers and attendees included scientists from major pharmaceutical firms, e.g., Abbott, MedImmune/Astra Zeneca, Bristol-Myers Squibb, Merck & Co., Pfizer, Sanofi-Aventis, Schering, GlaxoSmithKline, Eli Lilly, Hoffmann LaRoche, Novartis, Wyeth, and biotechnology companies, e.g., Ablynx, Medarex, Morphosys, GenMab, Amgen, Genentech, ImmunoGen, Agensys, Domantis, Biogen Idec, Centocor, LFB, Micromet, PDL Biopharma, Borean Pharma, Dyax Corp., Symphogen and Syntonix. Academic research groups at Imperial College London, University of Oxford, ETH Zürich, Scripps, Institute Cochin, Karolinska Institute, Utrecht University, Harvard Medical School, Massachusetts Institute of Technology, Baylor College, Paul Ehrlich Institute, University of California San Francisco, University of California San Diego, University of Nantes, University of Tours and Ludwig Institute were also represented, as were regulatory authorities, including the US Food and Drug Administration, National Institutes of Health and the Public Health Agency of Canada). The meeting was very interactive and included thoughtful exchanges during the different sessions and networking events.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.040 | 0.026 |
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".