Bibliographic record
Abstract
Abstract Empirical and experimental studies reporting the probability that some females remain unmated in field populations of insects (defined herein as mating failures) are reviewed in more than 100 species. The techniques used to quantify mating failures in the field are summarized, as well as factors that influence the probability that females mate during their lifetime. The existing empirical data provide partial support for hypotheses generated by theoretical models, although the trends observed in field populations are far more diverse and complex than predictions derived from ecological theory, e.g., the effect of population density on female mating success at small and large spatial scales is opposite. Mating success of females increases with the ratio of males in the population, but the relation between emergence time, sex ratio, and female mating success is variable. Females have evolved a broad range of physiological and behavioural adaptations to reduce mating failures, and exhibit a flexible context‐dependent response to constraints limiting mating success. The large number of studies in Lepidoptera suggests a higher mating success in butterflies than moths. Examples of high rates of mating failure include species with gynogenous reproduction, long range migration, pre‐reproductive maturation, male‐biased sex ratio, acquisition of resources essential for reproduction, and female flightlessness. Species with sessile females that mate and oviposit near their emergence site provide model systems to investigate the causes and demographic consequences of female mating failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".