Compensation by Cruciferous Plants is Specific to the Type of Simulated Herbivory
Bibliographic record
Abstract
The specificity of compensation by Brassica napus L. and Sinapis alba L. was investigated for herbivory by three biting and chewing herbivores: a small adult Coleoptera and a small and a large Lepidoptera larva. Phyllotreta cruciferae (Goeze) damaged the apical meristem and its defoliation of cotyledons was highly dispersed; the defoliation of Plutella xylostella L. was moderately dispersed over cotyledons; and Mamestra configurata (Walker) defoliated large contiguous areas of cotyledons. These types of herbivory were simulated in the field, and postdefoliation compensation by the plants was quantified: leaf length, relative growth rate of foliage, and seed production were measured. Plants were unable to compensate completely for meristem defoliation combined with highly dispersed cotyledon defoliation, and compensated better as cotyledon defoliation became less dispersed. Because compensatory responses to artificial defoliation were similar to and usually indistinguishable from those of insect herbivory, we conclude that the specificity of compensation is caused by the type of defoliation. Other interaction-specific processes such as transfer of growth-affecting chemicals from insect to plant need not be invoked. Sinapis alba compensated for defoliation better than B. napus because of inherent differences in compensatory responses, not because insects defoliate the two plant species differently.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".