{"id":"W2754917227","doi":"10.1016/j.tpb.2018.01.004","title":"Public goods games in populations with fluctuating size","year":2018,"lang":"en","type":"article","venue":"Theoretical Population Biology","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Population; Evolutionarily stable strategy; Natural selection; Selection (genetic algorithm); Public goods game; Constant (computer programming); Evolutionary dynamics; Population size; Evolutionary game theory","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":[0.003143546,0.0005305252,0.001373827,0.0008297489,0.0009851763,0.003571385,0.002097496,0.002663331,0.003776154],"category_scores_gemma":[0.02330807,0.0005397337,0.0007502006,0.0006979087,0.003812829,0.004755094,0.001983638,0.001973958,0.0003103932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003663,"about_ca_system_score_gemma":0.001079672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00244575,"about_ca_topic_score_gemma":0.002119689,"domain_scores_codex":[0.9982362,0.001027982,0.00005729501,0.0001963402,0.0002313155,0.0002509091],"domain_scores_gemma":[0.9843733,0.01245886,0.001293336,0.0006295913,0.0005250255,0.0007197948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001102614,0.00005342293,0.001299002,0.00006551629,0.00005478091,0.0002627486,0.0004662289,0.1181627,0.001287645,0.870553,0.001987341,0.005697283],"study_design_scores_gemma":[0.00007796838,0.00003556872,0.0006167315,0.00001348564,0.00002542811,0.00009464986,0.0002237745,0.2964512,0.0001647617,0.7013727,0.0008999595,0.00002388226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5810971,0.0006423131,0.3820399,0.006046505,0.000163548,0.0001104577,0.0002731514,0.0002263988,0.02940064],"genre_scores_gemma":[0.9846533,0.0002073809,0.0090714,0.0002106451,0.00006281056,0.0001097314,0.00005414732,0.00002800203,0.005602565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003776154,"threshold_uncertainty_score":0.01662487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04912811443577208,"score_gpt":0.3443473996830157,"score_spread":0.2952192852472436,"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."}}