Plant Feeding in an Omnivorous Mirid,<i>Dicyphus hesperus</i>: Why Plant Context Matters
Bibliographic record
Abstract
True omnivores that feed on both plant and animal tissues are not additive combinations of herbivore and predator (carnivore). Because true omnivores must distribute adaptive feeding decisions among two disparate tissue types, understanding the context that plants provide for foraging is important to understand their role in food webs. We varied prey and plant resources to investigate the plant context in an omnivorous true bug,Dicyphus hesperus. The contribution of plant species to fitness was unimportant in water acquisition decisions, but affected numbers of prey consumed over longer periods. In plant communities, in the absence of prey,D. hesperusmoved to plants with the highest resource quality. Unlike pure predators facing declining prey, omnivores can use a nondepleting resource to fund future foraging without paying a significant cost. However, the dual resource exploitation can also impose significant constraints when both types of resources are essential. The presence of relatively profitable plants that are spatially separate from intermediate consumer populations could provide a mechanism to promote stability within food webs with plant-feeding omnivores. The effects of context in omnivores will require adding second-order terms to the Lotka-Volterra structure to explicitly account for the kinds of interactions we have observed here.
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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) | 0.000 | 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 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".