Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it