Effects of mismatched mate availability cues on reproductive investment in female crickets
Bibliographic record
Abstract
Animals use cues experienced during early life to adaptively adjust their phenotype to the social environment they will likely experience. However, since animals often use multiple cues to assess the social environment, what happens if cues provide mismatched information? We examined the effects of mismatched cues of mate availability on behaviour and reproductive investment in female Pacific field crickets ( Teleogryllus oceanicus (Le Guillou, 1841))—a species that naturally encounters mismatched cues of mate availability due to the presence of singing and nonsinging male morphs in Hawaiian populations. In our experiment, females experienced either matched or mismatched acoustic cues (male song) and nonacoustic cues (physical presence of males) of mate availability during development. Mismatched cues did not affect female behaviour—females took longer to respond to male signals after experiencing cues of high mate availability regardless of whether or not acoustic and nonacoustic cues matched. However, mismatched cues did influence reproductive investment: females developed heavier ovaries only when both acoustic and nonacoustic cues indicated high mate availability.
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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 | 0.001 |
| 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 | 0.001 |
| 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".