{"id":"W2086195718","doi":"10.1016/j.jtbi.2012.07.016","title":"Co-evolution between sociality and dispersal: The role of synergistic cooperative benefits","year":2012,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds National de la Recherche Luxembourg; National Science Foundation","keywords":"Sociality; Biological dispersal; Altruism (biology); Kin selection; Population; Competition (biology); Biology; Social evolution; Inclusive fitness; Group selection; Selection (genetic algorithm); Ecology; Social psychology; Psychology; Evolutionary biology; Demography; Computer science; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003024476,0.0003890965,0.0007349201,0.0006973845,0.0007894103,0.002293878,0.0008485233,0.001864946,0.003365309],"category_scores_gemma":[0.01232244,0.0003447995,0.0005237645,0.000485311,0.002088218,0.002189915,0.002265547,0.001121023,0.000193351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005890589,"about_ca_system_score_gemma":0.0006357372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008599177,"about_ca_topic_score_gemma":0.001623494,"domain_scores_codex":[0.9987011,0.0008342704,0.00003307763,0.000192588,0.0001013419,0.0001376574],"domain_scores_gemma":[0.9884232,0.008272973,0.001241846,0.0006288065,0.0003970343,0.001036214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001649754,0.001061296,0.255713,0.0007073546,0.002206539,0.004001008,0.004016365,0.0803586,0.04598845,0.4865856,0.002982087,0.11473],"study_design_scores_gemma":[0.0004080276,0.0009395478,0.2726279,0.000114887,0.00119755,0.003984758,0.003605715,0.2193818,0.002130588,0.4913965,0.00395637,0.0002562341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792193,0.0006294805,0.01159653,0.001514795,0.00002701565,0.00001366841,0.00002488157,0.00001597908,0.006958542],"genre_scores_gemma":[0.9984683,0.0001063583,0.0009724729,0.00005590658,0.0000114992,0.000005331228,0.000005089704,0.000002822688,0.0003721146],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003365309,"threshold_uncertainty_score":0.01599514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588638046767177,"score_gpt":0.3173436417478022,"score_spread":0.3014572612801305,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}