Bibliographic record
Abstract
The promise of biotechnology has been slow to be realized, but some commercialized products are finding their way to supermarket shelves. Nevertheless, the future potential remains in the realm of speculation and may be on the verge of delivering some incredible benefits. Since the world population growth is predicted to double in the next 50 years, primarily in developing nations, food resources will become critical. In view of this prediction, we may need every trick in the book to feed the masses, which means either more land (wetlands, forests, and rain forests) will fall to the plow or there will need to be an increase in yields. Concurrently, a decrease in postharvest losses would also be crucial. Various authorities have estimated that 25% to 80% of harvested fruits and vegetables are lost due to damage and spoilage. Early biotech successes were developing plants with enhanced insect resistance (cotton, corn, and potato) and virus resistance (squash and papaya) and improved herbicide tolerance (cotton, soybean, and corn). The only commercialized transgenic fruit engineered for improved postharvest quality so far is the tomato. Future goals for biotechnology include increasing yield, extending shelf life, improving nutritional and flavor quality, and producing specialty proteins or other compounds. Genetically engineered food, however, has met rancorous resistance in Europe, New Zealand, and elsewhere; although, it is somewhat tolerated in the U.S. The U.S., Canada, and Japan lead the world in biotech acreage, with biotechnology accounting for 40% of cotton, 39% of soybeans, and 20% of corn acreage in the U.S. and 73 million acres worldwide.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.199 | 0.133 |
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".