Bibliographic record
Abstract
Legumes are members of the family Fabaceae or Leguminosae and include economically important grain legumes, oilseed crops, forage crops, shrubs, and tropical or subtropical trees. Legumes are a rich source of quality protein for humans and animals. They also enrich the soil by producing their own nitrogen in symbiosis with nitrogen-fixing bacteria. International centers and national institutes collect, maintain, distribute, and produce high-yielding legumes (grain-pulses, oilseeds, forages, nutraceuticals, medicinal shrubs, and trees). Legume breeders are confined within the primary gene pools (GP-1) in their varietal improvement programs and have not exploited secondary gene pools (GP-2), tertiary gene pools (GP-3), or quaternary gene pools (GP-4). Legumes are also an excellent source of timber, medicine, nutraceuticals, tannins, gums, insecticides, resins, varnish, paints, dyes, and eco-friendly by-products such as soy diesel. Three forage crops, Medicago truncatula, Lotus japonicus, and Trifolium pratense, are model legumes for phylogenetic studies and genome sequencing. This paper concludes that a "protein revolution" is needed to meet the protein demands of the world.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".