{"id":"W2023420371","doi":"10.5539/mas.v3n2p75","title":"Genetic Algorithm for Document Clustering with Simultaneous and Ranked Mutation","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Mutation; Mutation rate; Computer science; Genetic algorithm; Population; Similarity (geometry); Local optimum; Operator (biology); Chromosome; Algorithm; Data mining; Correlation clustering; Document clustering; Artificial intelligence; Machine learning; Genetics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003809679,0.0001682577,0.0001535073,0.0001640675,0.0004621596,0.0004450702,0.0008670636,0.00003242486,6.947802e-7],"category_scores_gemma":[0.00002520722,0.0001451263,0.0000156287,0.0005458069,0.0002567104,0.0004529669,0.0002332509,0.0001025399,0.000003674752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276816,"about_ca_system_score_gemma":0.0001133646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004666106,"about_ca_topic_score_gemma":0.000002651375,"domain_scores_codex":[0.9978861,0.0000103948,0.0001786679,0.000695156,0.0006420825,0.0005876473],"domain_scores_gemma":[0.9990129,0.0001256496,0.00006644056,0.000464268,0.0001392376,0.0001914826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001507335,0.00001585674,5.322682e-7,0.000006661441,0.000002585545,0.00001556092,0.0008522876,0.0539804,0.02199291,0.0003882947,6.751121e-7,0.9227291],"study_design_scores_gemma":[0.0007206337,0.0002562548,0.0001481558,0.00001043352,0.000002676809,0.00007826385,0.00003459772,0.9826735,0.003991817,0.01184839,0.00003105275,0.0002041749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005962344,0.00004635018,0.9926939,0.0002010161,0.00004401524,0.0006695747,0.000001249596,0.0001490425,0.0002325155],"genre_scores_gemma":[0.5061201,0.000003353551,0.493671,0.0001027056,0.00001860915,0.00004120782,3.229445e-7,0.000005672576,0.00003708563],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9286931,"threshold_uncertainty_score":0.5918077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040636211806242,"score_gpt":0.2725208109599084,"score_spread":0.262114448841846,"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."}}