Perspective: Matching, Mate Choice, and Speciation
Bibliographic record
Abstract
Matching was developed in the 1960s to match such entities as residents and hospitals, colleges and students, or employers and employees. This approach is based on "preference lists," whereby each participant ranks potential partners according to his/her preferences and tries to match with the highest-ranking partner available. Here, we discuss the implications of matching for the study of mate choice and speciation. Matching differs from classic approaches in several respects, most notably because under this theoretical framework, the formation of mating pairs is context-dependant (i.e., it depends on the configuration of pairings in the entire population), because the stability of mating pairs is considered explicitly, and because mate choice is mutual. The use of matching to study mate choice and speciation is not merely a theoretical curiosity; its application can generate counter-intuitive predictions and lead to conclusions that differ fundamentally from classic theories about sexual selection and speciation. For example, it predicts that when mate choice is mutual and the stability of mating pairs is critical for successful reproduction, sympatric speciation is a robust evolutionary outcome. Yet the application of matching to the study of mate choice and speciation has been largely dominated by theoretical studies. We present the hamlets, a group of brightly colored Caribbean coral reef fishes in the genus Hypoplectrus (Serranidae), as a particularly apt system to test empirically specific predictions generated by the application of matching to mate choice and speciation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".