Bibliographic record
Abstract
This article presents an overview of the legal liability issues in torts and patent law that arise from the use of transgenic crops produced by agricultural biotechnology. Torts—A tort is a civil legal action whereby the claimant alleges injury or wrong, arising independent of contract, to the person or property of the claimant. The article begins with legal liability claims for damage to property, damage to persons, and damage to economic interests (markets) that may arise with the use of transgenic crops. The tort theories discussed include the legal claims of trespass, strict liability, negligence, private nuisance, and public nuisance. With respect to each tort theory, the discussion points out unique legal issues that are likely to exist specifically because the litigation involves agricultural biotechnology. Patent Infringement—The article ends by focusing on four patent infringement cases that courts in Canada and the USA have decided regarding farmers who used patented seed from agricultural biotechnology without permission of the patent holder. As of May 2003, these are the only four patent infringement cases that have resulted in formal legal opinions by courts construing patent and antitrust laws in the context of farmers saving seeds protected by patents.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".