Leaf damage and prey type determine search effort in <i>Orius tristicolor</i>
Bibliographic record
Abstract
Abstract Components of search effort were determined for adult females of Orius tristicolor (White) (Hemiptera: Anthocoridae) on bean, Phaseolus vulgaris L., leaves with either western flower thrips, Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) or twospotted spider mites, Tetranychus urticae (Koch) (Acari: Tetranychidae) as prey. In the absence of prey, females of O. tristicolor allocated significantly more search time to leaves damaged by western flower thrips than to leaves damaged by twospotted spider mites, artificially damaged leaves or undamaged leaves. In the presence of prey, search time increased with increasing amounts of leaf damage for both prey species, but was not affected by prey species. Amounts of leaf damage or type of prey did not affect giving‐up‐time. The proportion of predators that successfully located thrips increased with increasing amounts of thrips damage on leaves. Females of O. tristicolor appeared to follow some simple, behavioural rules‐of‐thumb for allocation of search effort. The presence and type of damage determined the initial effort allocated to searching a leaf. Subsequent effort was determined by successful capture of prey, regardless of species. The implications of these results for application of Orius spp. for biological control are discussed.
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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 | 0.000 |
| 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".