Bibliographic record
Abstract
As drug companies move their R&D operations out of Quebec in rapid succession, Canadian authorities are trying to sustain the region’s life sciences community. In the latest effort, the government, along with AstraZeneca and Pfizer, is committing $100 million over five years to establish the NeoMed Institute, a nonprofit research center in Montreal. NeoMed aspires to bridge the gap between basic research and early clinical studies for drug candidates by offering universities and biotech start-ups funding and services. The hope is to eventually restore some of the hundreds of R&D jobs the region has lost in the past two years. AstraZeneca and Pfizer, along with any other big pharma partners that join NeoMed, will have an option to license molecules developed there. AstraZeneca is kicking in its former neuroscience research facility, which was focused on developing small molecules, along with $5 million and intellectual property for three potential pain drugs. ...
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.874 | 0.838 |
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".