Egg size affects larval performance in a coleopteran parasitoid
Bibliographic record
Abstract
Abstract 1. Optimal progeny size models assume that the more eggs a female produces, the lower the amount of resource allocated per egg. As egg size generally correlates with the fitness of the emerging immature, this trade‐off can be expressed as a choice between the production of numerous low quality or fewer high quality progeny. 2. The first‐instar larvae of the coleopteran parasitoid Aleochara bilineata have to search for and parasitise dipteran pupae. The present study found a positive correlation between egg size and larval weight, but not between egg size and development time. Larger first‐instar larvae survived longer, were more active, and found and parasitised their host more rapidly. 3. Female A. bilineata may invest smaller larvae in conditions of high host density and low intraspecific competition, but investing fewer, larger larvae would bring them more fitness when hosts are scarce and competition high.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".