Bibliographic record
Abstract
across the country. Pfizer Canada is a wholly-owned subsidiary of Pfizer Inc. with global headquarters in New York City. Pfizer Inc. and its affiliates discover, manufacture and market leading prescription medicines for humans and animals, as well as many of the world's best-known health care products. Pfizer Canada invested in excess of $152 million in R&D during 2002, making it the number one R&D pharmaceutical company in Canada. Pfizer Canada’s R&D network includes partnerships with Canadian universities, research hospitals and biotech companies in every province and its research activities cover all major therapeutic areas. Pfizer Canada appreciates the opportunity to participate in the dialogue relating to the proposed amendments to the Competition Act. In preparing these comments, Pfizer Canada has benefited from the advice of our external competition law counsel, Frank P. Monteleone, a senior partner at Cassels Brock & Blackwell LLP. Any questions concerning these submissions may be directed to me at (514) 426-6983 or Mr. Monteleone at (416) 869-5727. Yours truly,
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.036 | 0.015 |
| Insufficient payload (model declined to judge) | 0.274 | 0.128 |
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".