Patch exploitation strategy by an egg parasitoid in constant or variable environment
Bibliographic record
Abstract
Abstract. 1. In this paper, the foraging behaviour (the proximal mechanisms involved in patch‐leaving rules and the egg dispersion) of an egg parasitoid, Anaphes victus , was analysed in environments containing either patches of constant quality (i.e. predictable environment) or patches of variable quality (i.e. unpredictable environment) in order to determine the motivational mechanisms used in patch‐leaving strategies. 2. Comparison of the patch exploitation strategy of A. victus between the different habitats suggested that the response of A. victus to a given patch quality strongly depended on its past experiences. Females allocated more time and more eggs in a mixed quality patch after experiencing a poor quality patch than after experiencing a good quality patch. In a poor quality patch, females superparasitised more frequently after experiencing a poor quality patch than after experiencing a good quality patch. In a good quality patch, A. victus females laid more eggs after having visited two poor quality patches than after visiting good quality patches. 3. Recent foraging experiences are used to estimate both the availability and spatial distribution of hosts in the environment and adjust foraging decisions accordingly. The observed variability in the patch‐leaving rules within the same species stresses the importance of previous experience when describing behaviours of female parasitoids.
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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".