{"id":"W2944911880","doi":"10.1007/978-3-030-19823-7_38","title":"Optimizing Self-organizing Lists-on-Lists Using Enhanced Object Partitioning","year":2019,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Context (archaeology); Object (grammar); Hierarchy; Data structure; De facto; Theoretical computer science; Hierarchical clustering; Information retrieval; Field (mathematics); Cluster analysis; Data mining; Artificial intelligence; Programming language; Geography; Mathematics","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":[0.0003618772,0.0006935466,0.001057583,0.0006071178,0.0007334405,0.001025774,0.001860954,0.0008394787,0.004872538],"category_scores_gemma":[0.001029816,0.0004783786,0.0004664155,0.001121799,0.0003250067,0.001273202,0.001103752,0.00053253,0.0007764739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007035767,"about_ca_system_score_gemma":0.0007018292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00333003,"about_ca_topic_score_gemma":0.005906921,"domain_scores_codex":[0.9997306,0.00006334645,0.00001380009,0.00004443509,0.00008869851,0.00005909372],"domain_scores_gemma":[0.9993966,0.0003007704,0.00004361328,0.00009632591,0.0001213712,0.00004131137],"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":[0.0002612194,0.0002409265,0.0005324091,0.0001472628,0.00005421641,0.00005332529,0.00007745122,0.7801761,0.01205432,0.007288592,0.005379391,0.1937348],"study_design_scores_gemma":[0.00001320939,0.00005103499,0.00009214279,0.000002926798,0.000007684626,0.00001493303,0.00001557385,0.9960239,0.001758927,0.001442276,0.0005727677,0.000004594306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08709462,0.0006002715,0.9003512,0.0001435563,0.0001091659,0.0001155397,0.0001493877,0.002264185,0.009172092],"genre_scores_gemma":[0.5550278,0.0002157551,0.4358622,0.00009478618,0.0000599504,0.0001970099,0.0004355821,0.0004119154,0.007695024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004872538,"threshold_uncertainty_score":0.01630026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01342130486871757,"score_gpt":0.266610094672609,"score_spread":0.2531887898038914,"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."}}