Bibliographic record
Abstract
The Ecology and Evolution of Inducible Defenses. Ralph Tollrian and C. Drew Harvell, eds. Princeton University Press, Princeton, 1999. 383 pp., $29.95 US (paperback). ISBN 0-691-00494-3. It's hard not to be impressed by the striking forms that inducible defenses sometimes take in organisms as diverse as protists, plants and animals. Chemical cues from both predators and competitors can induce dramatic and seemingly adaptive changes in morphology, in chemistry, and in behavior. Tollrian and Harvell have responded to the growing interest in these phenomena by assembling an overview of the many taxa in which induced defenses occur and the various factors that might favor their evolution. Given our own interests in predator-induced defenses, we both received this book with anticipation. Tollrian and Harvell clearly encouraged authors to focus on a common theme. As they note, four criteria must be met for inducible defenses to evolve: i) agents of selection (e.g., predators or competitors) must vary in space or time, ii) cueing mechanisms must be reliable, iii) induced defenses must yield a benefit, and iv) induced defenses must incur a cost, otherwise they should become fixed. Most authors adhere to this theme, but as an unfortunate consequence the later data chapters begin to sound repetitive because, although the taxa and traits change, the script remains more or less the same. Clearly, if inducible defenses do exist in a taxon, then all four criteria must have been met for them to have evolved. So the only real surprises are how the criteria are met by different organisms, or how unexpected are the forms that costs or trade-offs take.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.550 | 0.493 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".