Bibliographic record
Abstract
Social interaction is like a dance between two or more individuals who communicate with each other by sending and receiving information. Social cues are multisensory, involving combinations of visual, olfactory, auditory, and tactile information. One individual transmits information through its behavior and the other interprets it, providing a behavioral response that contains further social information, and the dance continues. Unlike nonsocial behavior (e.g., a lone worm moving along a temperature gradient), social behavior is complex because the transmitter of the information changes the receiver’s subsequent behavior. From a statistical perspective, like individual behavior, variation in social behavior can be partitioned into its genetic and environmental contributions, and their interactions (1, 2). However, for social behavior, the social environment is a critical part of the equation (3, 4). Social interactions result in indirect genetic effects (IGEs), when the genetics of one individual affects another’s behavior (3, 4). IGEs are important for the evolution of social behavior; however, a mechanistic understanding of IGEs is lacking (5, 6). Which phenotypes should be measured when social interactions are an emergent property of the group? What genes and pathways are involved in IGEs, and are patterns of selection found in their DNA sequences? How can we validate and functionally investigate the role of genetic variants in a group setting? In PNAS, Avalos et al. (7) use genome-wide association studies (GWAS) in the eusocial honey bee to uncover genomic regions defined by colony allele frequencies that influence colony aggressive behavior. Social behavior is exhibited by many organisms, from microbes to humans (8). Factors that affect social interactions include components of the social environment (e.g., density, space, and group social structure) and the physical environment (e.g., temperature and humidity) (9). Social experiences can affect different levels of biological organization (e.g., neural transmission, gene … [↵][1]1Email: marla.sokolowski{at}utoronto.ca. [1]: #xref-corresp-1-1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".