Female genomic response to mate information
Bibliographic record
Abstract
Females should be choosier than males about prospective mates because of the high costs of inappropriate mating decisions. Both theoretical and empirical studies have identified factors likely to influence female mate choices. However, male-male social interactions also can affect mating decisions, because information about a potential mate can trigger changes in female reproductive physiology. We asked how social information about a preferred male influenced neural activity in females, using immediate early gene (IEG) expression as a proxy for brain activity. A gravid female cichlid fish (Astatotilapia burtoni) chose between two socially equivalent males and then saw fights between these two males in which her preferred male either won or lost. We measured IEG expression levels in several brain nuclei including those in the vertebrate social behavior network (SBN), a collection of brain nuclei known to be important in social behavior. When the female saw her preferred male win a fight, SBN nuclei associated with reproduction were activated, but when she saw her preferred male lose a fight, the lateral septum, a nucleus associated with anxiety, was activated instead. Thus social information alone, independent of actual social interactions, activates specific brain regions that differ significantly depending on what the female sees. In female brains, reproductive centers are activated when she chooses a winner, and anxiety-like response centers are activated when she chooses a loser. These experiments assessing the role of mate-choice information on the brain using a paradigm of successive presentations of mate information suggest ways to understand the consequences of social information on animals using IEG expression.
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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) | 0.004 | 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".