Bibliographic record
Abstract
There is a grey area for inventors - or more realistically, their employers - between the time when they know they have a good idea that will probably work, and having a demonstrably new invention that will be patentable. This leaves them with the challenge as to when to file for a patent and what it can cover. It has become common practice for the chemical, biotechnology, and drug industries to file for a patent (the "genus" patent) when the inventors have a discernible group of materials and compounds that can do "something," and which satisfies the requirements for utility, novelty and unobviousness, and then to continue working on those compounds in order to tease out best candidates with specific properties. In 'Apotex Inc. v. Sanofi-Synthelabo Canada Inc.', [2008] S.C.J. No. 63, the innovator company Sanofi had obtained a genus patent covering a large group of compounds on the basis of years of work and sound prediction. In the genus patent there was no distinction drawn between the effects of different isomers, where the compounds have the same chemical formula, but one version rotates polarised light to the right, "dextrorotatory," and the other to the left, "levorotatory." One version of the compound can be imagined as a mirror image of the other.
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 | 0.031 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.019 |
| Insufficient payload (model declined to judge) | 0.062 | 0.017 |
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".