Conference 2015: Drug Discovery and Development in the Post Genomic Era. An international symposium held jointly by CSPS and CC-CRS, May 26-28, 2015, Toronto, ON, Canada
Bibliographic record
Abstract
Plenaries and Special Presentations:Shana Kelley, University of Toronto: "New Technologies for Ultrasensitive Analysis of Clinically-relevant Biomolecules"Richard Hargreaves, BIOGEN IDEC: "Imaging in CNS Drug Discovery and Development"Roger Williams: CSPS Lifetime Achievement Award - "0.5 X 102: Looking Back and Forward"Neal Davies, University of Manitoba: CSPS Award of Leadership in Canadian Pharmaceutical Sciences - "30 Years of Coffee, Beer and Serendipity in Pharmacy Research"Conference Sessions:Special CSPS Session: The Future of Pharmaceutical Sciences1. The Evolving Business of Pharmaceuticals2. Analysis of Peptide and Protein Drug Targets by LC/MS/MS3. Mucosal Drug Delivery4. New Methodologies of Genome Wide Target Validation5. Regulatory Updates and Developments6. Antibody-based Therapeutics7. Imaging in Drug Delivery8. Nuclear Receptors in Drug Discovery9. IV-IVC Modeling and Simulation as a Tool to Facilitate Drug Development and Marketing10. Pharmacogenomics in Drug Development11. Bioavailability of Novel Dosage Forms
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.016 |
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".