Phytochemical diversity of insect defenses in tropical and temperate plant families
Bibliographic record
Abstract
Phytochemical diversity and redundancy One of the most intriguing features of the chemical ecology of plant–insect interactions is the remarkable number of different phytochemical defenses found in plants. A single plant may contain five or six biosynthetic groups of secondary metabolites and within each group these defenses may include many structurally related analogs and derivatives. Across the different species of higher plants, there is a bewildering array of different substances and modes of actions of substances. During the course of our research on phytochemical defenses in specific plant families, we have become interested in the raison d'être for this diversity, as have many other researchers (Romeo et al. , 1996). How much do we know about different types of defenses in plants? How did they arise? Are some of these secondary metabolites “redundant,” with no function? How do they interact with one another? In this chapter, we will address some of these issues with observations on the defenses of several plant families against insects, using results from our own research and the published literature. In general, we do not have a comprehensive picture of the different types of defenses in plants. Over a decade ago, Soejarto and Farnsworth (1989) estimated that of the 250 000 species of flowering plants, only 5000 species had been thoroughly investigated according to the Natural Product Alert (NAPRALERT) database, leaving 98% of species with potential for phytochemical discovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".