Bibliographic record
Abstract
Supporters of GM agriculture have had a long row to hoe in achieving public acceptance for the safety of this important technology. Controversy has surrounded the foundational technology of recombinant DNA methods, the application of genetic engineering to crop plants and livestock, the safety of GM “Frankenfoods” as sources of human and animal nutrition, the potential environmental threats posed by the possible development of GM “superweeds,” and the corporate control over GM agriculture exercised by a relatively small number of agricultural companies armed with vast financial resources and powerful patent portfolios. Nevertheless, as exemplified by the United States and Canadian Supreme Court cases, Diamond v. Chakrabarty, J.E.M. v. Pioneer Hi-Bred, Monsanto Canada v. Schmeiser, and Monsanto v. Geertson, the law, and the society it reflects, have finally managed to accommodate the important technology of GM agriculture. However, a new paradigm in biological science — synthetic biology — has begun to remake the face of GM agriculture. Synthetic biology seeks to purge biology of some of its fundamental inefficiencies through the rigorous application of engineering principles. Rather than tinkering around the edges, biological engineering would remake living organisms from first principles, and employ standard parts to make qualitatively new biological devices and systems. Traditional arguments that GM crops and livestock are simply slightly-modified versions of their conventional counterparts may no longer be either appropriate or accurate in the face of synthetic biological approaches to engineering new plants. Moreover, both synthetic biology and do-it-yourself biology (“DIYbio”) seek to shift biological research and development out of traditional laboratories and the hands of credentialed biologists, and, instead, allow any interested and motivated user to become a research biologist, biotinkerer, or synthetic biological engineer. Home and community laboratories are already springing up at a rapid rate, and farm laboratories are sure to follow, as participation in this new, open, and democratized movement burgeons. In short, large numbers of individual and collaborating users, spread over many small and local laboratories, are beginning fundamentally to reengineer genes, cells, organisms, and systems composed of organisms or their substituent parts. The comfortable acceptance of GMOs at which society has only recently begun to arrive may soon be misplaced in the face of both fundamentally new scientific approaches and the democratization of innovation. The results for agriculture may be beneficial: enhanced rates of agricultural innovation through new biological approaches and wide participation. Moreover, synthetic biological agriculture (“synagriculture”) may prove to be as safe as GM agriculture or even conventional agriculture. However, assumptions about current GM crops and livestock may not easily apply to synthetic versions, nor may the current paradigm of GM regulation be possible when innovation becomes atomized among millions of farmers. Some of the “settled” legal issues surrounding GM crops and livestock may have to be revisited as new perceived or actual threats and benefits arise. One irony may be that the same patent system that has so often been criticized in the past for providing agricultural companies with too much control over farmers may soon represent one of the most effective methods for monitoring and regulating GM agricultural innovation. Although some farmer innovators may eschew patent coverage for their agricultural inventions, others may opt to seek patent protection for their innovative new synthetic crops and livestock. Because the USPTO will have to examine any new GM crop inventions prior to issuing letters patent, disclosures to the USPTO synthetic biological inventors who opt for patent protection may become a vital centralized locus for monitoring and regulating otherwise highly-decentralized synagricultural innovation. New methods of biological engineering and new models of user, collaborative, and open innovation are soon to affect the trajectory of GM agricultural innovation. Even if such changes turn out to be salutary, they will be changes nevertheless. To ensure that society receives the full benefits of open and democratized synthetic biological innovation in crops and livestock, it would be well and wise for the law to prepare itself to reexamine the brave new world of synagriculture with brand new eyes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".