Bibliographic record
Abstract
Abstract Since the 1980s, patent offices in most developed countries (with some exceptions, particularly in Latin America) have been granting patents over both genomic and modified deoxyribonucleic acid (DNA) sequences based on the legal understanding of the difference between invention and discovery, which does not necessarily accord with the same concepts among scientists. Recent litigation in USA has, however, cast uncertainty over this conclusion, particularly with respect to genomic DNA sequences. Patents over the process of using these sequences to identify the risk of contracting a disease is even more uncertain. Controversy continues over the role that both DNA sequence and associated method patents play in encouraging innovation. This is particularly true in clinical genetics in which the patent holder is rarely the first to make a genetic test available to patients. Key Concepts: The legal understanding of the difference between an ‘invention’ and a ‘discovery’ does not correspond to how scientists appreciate the difference. Patent offices in most developed countries have been granting patents over both DNA sequences (in both genomic and modified form such as cDNA) and methods of using them since the 1980s. Only recently have these been challenged in court in USA with uncertain results.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".