Plant Products for Health
Bibliographic record
Abstract
Abstract Plants play an essential role in many aspects of human health, including nutrition and medicine. Advancements in biology and engineering have hastened research regarding plant‐based medicines. More specifically, biotechnology has played a significant role in identifying new ways that plants can be utilised. There are three major directions that plants are being developed to improve our health. The first concerns the use of plants as production platforms for modern pharmaceuticals. The second deals with plant biofortification strategies to prevent malnutrition and defeat chronic diseases. The third describes the identification and analysis of bioactive compounds derived from plants in modern medicine. All three sections bring to light mankind's continuous reliance on plants for our current and future health needs. Key Concepts Plants play an essential role in many aspects of human health, including nutrition and medicine. Plants can be used as production platforms for biopharmaceuticals. Biofortified crops such as Golden Rice and Banana21 can prevent Vitamin A deficiency. Plant secondary metabolites can be used as a source of bioactive molecules to improve human health. However, significant opposition to biotechnology has slowed the research and development of plant-based health products.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.250 | 0.122 |
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".