Influence of body size and familiarity on mating and reproductive parameters in the zig-zag ladybird beetle, <i>Menochilus sexmaculatus</i> (Coleoptera: Coccinellidae)
Bibliographic record
Abstract
Body size often indicates an individual’s quality and so mate selection is typically for larger individuals, including in ladybirds (Coccinellidae). Many organisms including ladybirds are also are known to refuse mating attempts with familiar individuals, but whether at the expense of mating with larger individuals is not clear. We assessed the cumulative effect of body size and familiarity on mating and reproductive behaviour in the zig-zag ladybird beetle (Menochilus sexmaculatus (Fabricius, 1781) = Cheilomenes sexmaculata (Fabricius, 1781)). For this study, individuals were separated into small- and large-bodied individuals and allowed to mate in all possible combinations. Furthermore, following the first mating, an immediate second mating was provided to the males, with either a familiar (same as in the first mating) or an unfamiliar (new female with the same mating status) female. Mating and reproductive parameters were recorded. The mating duration was longer with familiar partners than unfamiliar individuals. Mating duration was shorter for the second mating, suggesting that mating and ejaculate transfer are costly, to assure higher reproductive success. In this ladybird beetle, familiarity modified mating duration, whereas fecundity and egg viability were influenced by body size.
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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".