Bibliographic record
Abstract
Though many international environmental, political, and advocacy groups are focusing on international agreements and regulations to address the issues facing the world’s oceans, others have turned to technological-based solutions and are even finding ways to capitalize on the issue – with potentially significant consequences for international food system sustainability. AquaBounty Technologies, Inc., a biotechnology company, is positioning itself as a “food security game-changer”, marketing genetically engineered (GE) fish as a cheap food. The company has become the first to produce a commercially feasible and federally approved GE fish, known as AquAdvantage Salmon. The company gained approval for the commercial sale of the fish to humans from the U.S. Food and Drug Administration (FDA) in 2015 and the Canada Food Inspection Agency (CFIA) in 2016. Though this is the first approved use of GE technology in an animal for commercial sale and consumption by humans, it is certainly not the only one being investigated. Internationally, other private companies are working to develop more species of GE fish, shellfish, and even land-based farm animals. Regulation of these GE animals is complicated; for example, the FDA regulates GE animals under the new animal drug provisions of the Federal Food, Drug, and Cosmetic Act. While this is certainly a novel approach to address the increased pressures faced by the world’s oceans, extreme care and caution should be taken by the scientific and regulatory community in managing this new technology.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".