Effect of Plant Structure on Searching Strategy and Searching Efficiency of<i>Trichogramma turkestanica</i>
Bibliographic record
Abstract
When searching for hosts on a plant, female parasitoids use strategies to maximize efficiency. Searching strategies include the expressed behaviors, the time budget associated with each behavior, the time allocated to the different plant parts and the exploration sequence of plant parts. Searching efficiency refers to the time taken to find the first egg, the number of eggs found per foraging time unit and the re-encountering frequency of eggs during a foraging period. This study examines the effect of artificial simple (few leaves and connections) and complex plant structures (more leaves and connections) on searching strategy and searching efficiency of the egg parasitoid Trichogramma turkestanica Meyer (Hymenoptera: Trichogrammatidae). Analyses of frequency and duration of behaviors associated with searching on artificial plants of different complexities were performed. Plant structure had no effect on time associated with locomotion behaviors such as walking, standing and flying. However, it had an impact on the area searched, which was significantly greater on simple plant structure. Also, time spent on a leaf without encountering an egg was greater on complex plant structure compared to simple one. No significant differences were found between simple and complex plant structures regarding time spent walking on the different plant parts such as twigs, limbs, leaf perimeters, and limbs of inferior and superior leaf sides. Results showed that female parasitoids spent less time actively exploring complex than simple plants. Encountering and re-encountering frequencies of eggs were significantly greater on simple than on complex plant structure. Plant structure had no effect on handling time of eggs. This study demonstrates that plant structure can modulate activities inherent to searching and ovipositing, which in turn affects area searched per foraging time unit and therefore host finding success.
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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".