Bibliographic record
Abstract
There are over 400 thousand plant species on earth. Tens of thousands of these are used directly for food, medicine, construction materials, industrial products, and ornament. Some of the world's major crops have become unprofitable, and there is a need for new crops to meet the growing needs of the 21st century. New crops can be plants not used previously, new varieties of familiar crops, well known crops used for a new purpose, and crops cultivated in a new area, grown with new techniques or sold in new markets. There is excellent potential to utilize thousands of plants that are not yet well known. Promising new crops include all kinds of plants originating from all over the world. Examples are provided of recent new crops in eight major categories, including food, forage, medicine, wood & fibre, industrial purposes, fuel & energy, ornament, and environmental benefits. Because it is impossible to predict exactly which plants will be invaluable in the future, it is critical to maintain as many of the world's species and as much of their genetic diversity as possible. Measures should include largescale protection of natural landscapes. New crops will be important in the future to efficiently feed a growing population, to maintain human health, to meet economic demands, and to promote the protection of biodiversity & environment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".