{"id":"W7126268783","doi":"10.1109/iscmi67495.2025.11358636","title":"Graph Compression with a Genetic Algorithm: Exploring Fitness, Randomness, and Efficiency","year":2025,"lang":"","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Merge (version control); Graph; Set (abstract data type); Fitness function; Genetic algorithm; Data compression","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000431567,0.0007257378,0.0008272189,0.00054885,0.001238183,0.001067486,0.001522493,0.0001930351,0.00009066358],"category_scores_gemma":[0.00002989213,0.0005240421,0.0001237795,0.001974826,0.0005180401,0.001702582,0.002231945,0.0004873995,0.0000194822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004630106,"about_ca_system_score_gemma":0.0003271595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002822154,"about_ca_topic_score_gemma":0.00001150806,"domain_scores_codex":[0.9954243,0.0002440564,0.0008020831,0.001783633,0.000803805,0.0009421651],"domain_scores_gemma":[0.9972803,0.0002638015,0.0002455453,0.001567446,0.0002890991,0.0003538282],"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":[0.0001804683,0.0003866882,0.0009706648,0.0002261679,0.00008440309,0.0001118609,0.0006367537,0.0006791851,0.0001528592,0.004092398,0.001283485,0.9911951],"study_design_scores_gemma":[0.009227356,0.0004734744,0.01440013,0.002510896,0.000155434,0.00009954409,0.000332942,0.9590439,0.001815444,0.002842861,0.00797335,0.001124729],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0129968,0.01073194,0.9706222,0.0005963612,0.001669908,0.0008567537,0.00001749634,0.0002502653,0.002258296],"genre_scores_gemma":[0.4092036,0.009957531,0.5768231,0.0007408053,0.0003477073,0.0003935555,0.00002562091,0.00007764597,0.0024304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9900703,"threshold_uncertainty_score":0.9999695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769186209384433,"score_gpt":0.2341594768555719,"score_spread":0.2164676147617276,"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."}}