Bibliographic record
Abstract
Abstract The terpenoids are a family of natural products defined as being those possessing a carbon skeleton based on isoprene units. This skeletal feature stems from their biosynthesis from mevalonic acid. They serve a wide range of functions in nature including defensive resins, pheromones, antioxidants, and the pigments responsible for vision. It is not surprising therefore that they are also of considerable importance commercially and find application as flavors, fragrances, agrochemicals, and pharmaceuticals. Volatile terpenoids, such as geraniol, linalool, citronellol, citral, and the ionones and damascones, are important in the flavor and fragrance industry. Campholenic aldehyde, the precursor for a range of sandalwood odored materials, is an example of a key terpenoid intermediate. Currently, the best‐known terpenoid pharmaceutical is probably the anticancer drug paclitaxel. Industrial routes to terpenoid compounds comprise an interesting mixture of extraction from nature, partial synthesis from natural feedstocks and total synthesis from petrochemicals. In many instances, two or even all three of these possibilities exist in technocommercial equilibrium. Principal feedstocks include α‐pinene, β‐pinene, myrcene, limonene, isoprene, isobutylene, acetone, and acetylene. In this article, a brief summary of the biosynthesis of terpenoids is followed by a description of the main industrial routes to those of commercial importance. The remainder comprises a series of monographs on the commercially more important members of the family including monoterpenes, oxygenated monoterpenoids, sesquiterpenoids, diterpenoids, triterpenoids, carotenoids, and terpenoid degradation products, such as the ionones, damascones, and ambergris chemicals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.063 |
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".