Concurrent effects of familiarity and kinship on social affiliations in convict cichlid (Amatitlania siquia) young
Bibliographic record
Abstract
The cues associated with social familiarity and genetic relatedness and how they interact to influence the formation of social associations among individuals, and thus group composition and dynamics, is poorly understood. Here, we investigated the concurrent effects of social familiarity and kinship on social affiliations in free-swimming convict cichlid fish young or ‘fry’ (Amatitlania siquia) by pitting the cues of social familiarity and kinship against each other in a four-way choice apparatus. Individual focal fish were given a simultaneous choice to associate (‘shoal’) with conspecifics that were either socially familiar and kin (full sibs), socially unfamiliar and kin, socially familiar and not kin, or socially unfamiliar and not kin. Stimulus shoal preference differed depending on the body length of the focal fish; smaller fry exhibited no preference, whereas larger (more mobile) fry significantly preferred to associate with familiar kin. In the convict cichlid system, where brood mixing occurs in the wild, a preference to associate with familiar kin may confer fitness benefits to individuals, especially when fry become more mobile as they grow and encounter predators more often. Our results contribute to further our understanding of the roles of familiarity and kinship in the formation of social associations in the convict cichlid in particular and in animals in general.
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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 | 0.001 |
| 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".