Bibliographic record
Abstract
Terpenoids (isoprenoids) encompass more than 40 000 structures and form the largest class of all known plant metabolites. Some terpenoids have well-characterized physiological functions that are common to most plant species. In addition, many of the structurally diverse plant terpenoids may function in taxonomically more discrete, specialized interactions with other organisms. Historically, specialized terpenoids, together with alkaloids and many of the phenolics, have been referred to as secondary metabolites. More recently, these compounds have become widely recognized, conceptually and/or empirically, for their essential ecological functions in plant biology. Owing to their diverse biological activities and their diverse physical and chemical properties, terpenoid plant chemicals have been exploited by humans as traditional biomaterials in the form of complex mixtures or in the form of more or less pure compounds since ancient times. Plant terpenoids are widely used as industrially relevant chemicals, including many pharmaceuticals, flavours, fragrances, pesticides and disinfectants, and as large-volume feedstocks for chemical industries. Recently, there has been a renaissance of awareness of plant terpenoids as a valuable biological resource for societies that will have to become less reliant on petrochemicals. Harnessing the powers of plant and microbial systems for production of economically valuable plant terpenoids requires interdisciplinary and often expensive research into their chemistry, biosynthesis and genomics, as well as metabolic and biochemical engineering. This paper provides an overview of the formation of hemi-, mono-, sesqui- and diterpenoids in plants, and highlights some well-established examples for these classes of terpenoids in the context of biomaterials and biofuels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.017 |
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".