{"id":"W4280596745","doi":"10.1101/2022.05.15.492034","title":"Adaptive diversification and niche packing on rugged fitness landscapes","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada","keywords":"Diversification (marketing strategy); Niche; Fitness landscape; Ecological niche; Selection (genetic algorithm); Diversity (politics); Limiting; Fitness function; Ecology; Adaptive capacity; Coexistence theory; Biology; Evolutionary biology; Computer science; Mathematical optimization; Mathematics; Population; Business; Engineering; Artificial intelligence","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":[0.0004634492,0.0002616744,0.0005160108,0.001119455,0.000436959,0.001183485,0.0003499305,0.0005435684,0.001574362],"category_scores_gemma":[0.002348926,0.0003044495,0.0003315554,0.000474431,0.001701216,0.001219822,0.001099658,0.0005914597,0.0001206259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000409457,"about_ca_system_score_gemma":0.00009709753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002618915,"about_ca_topic_score_gemma":0.0001832671,"domain_scores_codex":[0.9997962,0.00007694904,0.00001136438,0.00004533063,0.00003618332,0.0000339388],"domain_scores_gemma":[0.9979239,0.00114222,0.0004480512,0.0002093231,0.00006325845,0.0002132706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004156405,0.0001522012,0.04264048,0.0004250096,0.0005727034,0.001890669,0.000943634,0.4812316,0.3092697,0.1301879,0.001206632,0.03106378],"study_design_scores_gemma":[0.00005525439,0.0001798142,0.06243109,0.00005056915,0.00007084249,0.001250275,0.0002818386,0.7515959,0.01323938,0.1690781,0.001674796,0.00009207988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790003,0.0004457905,0.01846606,0.000187011,0.000007458754,0.000007947136,0.00005971475,0.0000677807,0.001757955],"genre_scores_gemma":[0.9980938,0.00006989306,0.00162997,0.00002306207,0.000004747577,0.000008148366,0.00002463881,0.00001018686,0.000135671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001574362,"threshold_uncertainty_score":0.005266786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181186522212632,"score_gpt":0.2196596020952298,"score_spread":0.2078477368731035,"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."}}