{"id":"W2103434069","doi":"10.1109/lescpe.2006.280384","title":"Gregariousness vs. Social Intolerance: Towards New Dynamism in Swarm Optimizers","year":2006,"lang":"en","type":"article","venue":"","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Dynamism; Swarm behaviour; Novelty; Computer science; Set (abstract data type); Particle swarm optimization; Domain (mathematical analysis); Swarm intelligence; Order (exchange); Space (punctuation); Artificial intelligence; Social psychology; Psychology; Machine learning; Mathematics; Business; Epistemology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00118046,0.00046241,0.0005681696,0.0005782061,0.0004741177,0.001254674,0.0005620923,0.0007203577,0.0006297769],"category_scores_gemma":[0.001946113,0.0002564921,0.0006398427,0.0003722086,0.001975932,0.001962891,0.001578144,0.000959372,0.00007586988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004752802,"about_ca_system_score_gemma":0.0003073001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000375935,"about_ca_topic_score_gemma":0.0004030606,"domain_scores_codex":[0.9996243,0.0001661581,0.00002439724,0.00005282424,0.0000988406,0.00003346923],"domain_scores_gemma":[0.9993742,0.0002574596,0.0001746478,0.00006895408,0.00005751546,0.00006717297],"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":[0.00006649125,0.00004677592,0.003549924,0.00007971891,0.00009388142,0.0002020607,0.0006135054,0.143023,0.004933333,0.8220965,0.0008894064,0.02440559],"study_design_scores_gemma":[0.00004095857,0.0001005734,0.001678662,0.00002621924,0.00002320329,0.0001132348,0.000175125,0.5031385,0.0004365528,0.4908116,0.003427077,0.00002829741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1712927,0.001134536,0.8014805,0.002956787,0.0001120733,0.00004627575,0.00003490626,0.0000802875,0.02286202],"genre_scores_gemma":[0.9305503,0.0007698044,0.06491893,0.0002808269,0.0002711595,0.0001161249,0.00001995599,0.00005255994,0.003020278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001254674,"threshold_uncertainty_score":0.006242931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05241852859442708,"score_gpt":0.3009850100672993,"score_spread":0.2485664814728722,"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."}}